Navigating dandiset 000728: Visual Coding - Optical Physiology
import os
import json
import pathlib
import warnings
from typing import Union, Tuple, Literal
import h5py
import matplotlib
import numpy
import pandas
import pynwb
import remfile
import scipy.stats as scipy_stats
import matplotlib.pyplot as matplotlib_pyplot
from dandi.dandiapi import DandiAPIClient, RemoteAsset, RemoteDandiset
%matplotlib inline
Part 1: Getting oriented to the dataset
Alongside this notebook are included two JSON files that contain high-level metadata of the experiments - the conditions under which each was acquired.
# You may need to change these paths to the files depending on your current working directory
# These defaults assume the current working directory is the folder containing this notebook
asset_metadata_folder_path = pathlib.Path("asset_metadata")
experiment_containers_metadata_file_path = asset_metadata_folder_path / "experiment_containers_metadata.json"
visual_coding_sessions_metadata_file_path = asset_metadata_folder_path / "visual_coding_sessions_metadata.json"
with open(file=experiment_containers_metadata_file_path, mode="r") as fp:
experiment_containers_metadata = json.load(fp=fp)
with open(file=visual_coding_sessions_metadata_file_path, mode="r") as fp:
visual_coding_sessions_metadata = json.load(fp=fp)
Start by getting all the areas, which are referred to as "targeted structures".
all_targeted_structures = list(set([experiment_container["targeted_structure"] for experiment_container in experiment_containers_metadata]))
all_targeted_structures
['VISrl', 'VISl', 'VISam', 'VISp', 'VISpm', 'VISal']
Then use a similar approach to get all imaging depths, all cre lines, all reporter lines, all stimuli, and all session types
all_cre_lines = list(set([experiment_container["cre_line"] for experiment_container in experiment_containers_metadata]))
all_cre_lines
['Cux2-CreERT2', 'Fezf2-CreER', 'Scnn1a-Tg3-Cre', 'Rbp4-Cre_KL100', 'Slc17a7-IRES2-Cre', 'Vip-IRES-Cre', 'Nr5a1-Cre', 'Sst-IRES-Cre', 'Ntsr1-Cre_GN220', 'Rorb-IRES2-Cre', 'Pvalb-IRES-Cre', 'Tlx3-Cre_PL56', 'Emx1-IRES-Cre']
all_reporter_lines = list(set([experiment_container["reporter_line"] for experiment_container in experiment_containers_metadata]))
all_reporter_lines
['Ai94(TITL-GCaMP6s)', 'Ai162(TIT2L-GC6s-ICL-tTA2)', 'Ai93(TITL-GCaMP6f)-hyg', 'Ai93(TITL-GCaMP6f)', 'Ai148(TIT2L-GC6f-ICL-tTA2)']
all_session_types = list(set([session["session_type"] for session in visual_coding_sessions_metadata]))
all_session_types
['three_session_C', 'three_session_A', 'three_session_C2', 'three_session_B']
The following figure is a good reference for describing the main differences between each session type with respect to the visual stimuli used.

1.1 Experiment containers & sessions¶
The experiment container describes a set of 3 imaging sessions performed for the same field of view (ie. same targeted area and imaging depth in the same mouse that targets the same set of neurons).
Each experiment container has a unique session ID. This, together with the subject ID, will allow us to identify the NWB file from which to access the data from.
Using the metadata in this table, we can find all the experiment containers for this area and Cre line.
experiments_subset = [
experiment_container
for experiment_container in experiment_containers_metadata
if experiment_container["targeted_structure"] == "VISp" and experiment_container["cre_line"] == "Cux2-CreERT2"
]
experiment_subset_table = pandas.DataFrame(experiments_subset)
experiment_subset_table
| id | imaging_depth | targeted_structure | cre_line | reporter_line | donor_name | specimen_name | tags | failed | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 511510736 | 175 | VISp | Cux2-CreERT2 | Ai93(TITL-GCaMP6f) | 222426 | Cux2-CreERT2;Camk2a-tTA;Ai93-222426 | [] | False |
| 1 | 511510855 | 175 | VISp | Cux2-CreERT2 | Ai93(TITL-GCaMP6f) | 229106 | Cux2-CreERT2;Camk2a-tTA;Ai93-229106 | [] | False |
| 2 | 511509529 | 175 | VISp | Cux2-CreERT2 | Ai93(TITL-GCaMP6f) | 222420 | Cux2-CreERT2;Camk2a-tTA;Ai93-222420 | [] | False |
| 3 | 511507650 | 175 | VISp | Cux2-CreERT2 | Ai93(TITL-GCaMP6f) | 222424 | Cux2-CreERT2;Camk2a-tTA;Ai93-222424 | [] | False |
| 4 | 702934962 | 275 | VISp | Cux2-CreERT2 | Ai93(TITL-GCaMP6f) | 382421 | Cux2-CreERT2;Camk2a-tTA;Ai93-382421 | [] | False |
| 5 | 645413757 | 275 | VISp | Cux2-CreERT2 | Ai93(TITL-GCaMP6f) | 348262 | Cux2-CreERT2;Camk2a-tTA;Ai93-348262 | [] | False |
| 6 | 659767480 | 275 | VISp | Cux2-CreERT2 | Ai93(TITL-GCaMP6f) | 360565 | Cux2-CreERT2;Camk2a-tTA;Ai93-360565 | [] | False |
| 7 | 511510650 | 175 | VISp | Cux2-CreERT2 | Ai93(TITL-GCaMP6f) | 222425 | Cux2-CreERT2;Camk2a-tTA;Ai93-222425 | [] | False |
| 8 | 712178509 | 275 | VISp | Cux2-CreERT2 | Ai93(TITL-GCaMP6f) | 390323 | Cux2-CreERT2;Camk2a-tTA;Ai93-390323 | [] | False |
| 9 | 511510667 | 275 | VISp | Cux2-CreERT2 | Ai93(TITL-GCaMP6f) | 222420 | Cux2-CreERT2;Camk2a-tTA;Ai93-222420 | [] | False |
| 10 | 524691282 | 275 | VISp | Cux2-CreERT2 | Ai93(TITL-GCaMP6f) | 243293 | Cux2-CreERT2;Camk2a-tTA;Ai93-243293 | [] | False |
| 11 | 701412138 | 175 | VISp | Cux2-CreERT2 | Ai93(TITL-GCaMP6f) | 382421 | Cux2-CreERT2;Camk2a-tTA;Ai93-382421 | [] | False |
| 12 | 511510718 | 175 | VISp | Cux2-CreERT2 | Ai93(TITL-GCaMP6f) | 231584 | Cux2-CreERT2;Camk2a-tTA;Ai93-231584 | [] | False |
| 13 | 511510699 | 275 | VISp | Cux2-CreERT2 | Ai93(TITL-GCaMP6f) | 225037 | Cux2-CreERT2;Camk2a-tTA;Ai93-225037 | [] | False |
| 14 | 511510779 | 275 | VISp | Cux2-CreERT2 | Ai93(TITL-GCaMP6f) | 225036 | Cux2-CreERT2;Camk2a-tTA;Ai93-225036 | [] | False |
| 15 | 511510670 | 175 | VISp | Cux2-CreERT2 | Ai93(TITL-GCaMP6f) | 225037 | Cux2-CreERT2;Camk2a-tTA;Ai93-225037 | [] | False |
number_of_experiment_containers = len(experiment_containers_metadata)
number_of_experiment_containers
540
Let's look at one experiment container, imaged from Cux2, in VISp, from imaging depth 175 um.
experiment_container_id = experiments_subset[0]["id"]
experiment_container_id
511510736
sessions = [
visual_coding_session for visual_coding_session in visual_coding_sessions_metadata
if visual_coding_session["experiment_container_id"] == experiment_container_id
]
sessions_table = pandas.DataFrame(sessions)
sessions_table
| id | imaging_depth | targeted_structure | cre_line | reporter_line | acquisition_age_days | experiment_container_id | session_type | donor_name | specimen_name | fail_eye_tracking | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 501559087 | 175 | VISp | Cux2-CreERT2 | Ai93(TITL-GCaMP6f) | 103 | 511510736 | three_session_B | 222426 | Cux2-CreERT2;Camk2a-tTA;Ai93-222426 | True |
| 1 | 501704220 | 175 | VISp | Cux2-CreERT2 | Ai93(TITL-GCaMP6f) | 104 | 511510736 | three_session_A | 222426 | Cux2-CreERT2;Camk2a-tTA;Ai93-222426 | True |
| 2 | 501474098 | 175 | VISp | Cux2-CreERT2 | Ai93(TITL-GCaMP6f) | 102 | 511510736 | three_session_C | 222426 | Cux2-CreERT2;Camk2a-tTA;Ai93-222426 | True |
Let's get the id for the imaging session in this experiment container that used session type B.
session_id = [session for session in sessions if session["session_type"] == "three_session_B"][0]["id"]
session_id
501559087
Part 2: Streaming all the data for a single session
Start by defining some quick helper functions...
def read_dandi_file(*, asset: RemoteAsset) -> pynwb.NWBFile:
"""Remotely load the NWB file from a DANDI asset object."""
# Harmless warnings from PyNWB
warnings.filterwarnings(action="ignore", message="No cached namespaces found in .*")
warnings.filterwarnings(action="ignore", message="Ignoring cached namespace .*")
s3_url = asset.get_content_url()
byte_stream = remfile.File(url=s3_url)
file = h5py.File(name=byte_stream)
io = pynwb.NWBHDF5IO(file=file, mode="r", load_namespaces=True)
nwbfile = io.read()
return nwbfile
def read_visual_coding_ophys_file(
*, dandiset: RemoteDandiset, session_id: str, raw_or_processed: Literal["raw", "processed"] = "processed"
) -> pynwb.NWBFile:
"""Load the NWB file from DANDI given only the unique session ID in the Visual Coding - Optical Physiology dataset."""
if raw_or_processed == "processed":
asset = next(asset for asset in dandiset.get_assets() if f"ses-{session_id}" in asset.path and "behavior" in asset.path)
elif raw_or_processed == "raw":
asset = next(asset for asset in dandiset.get_assets() if f"ses-{session_id}" in asset.path and "behavior" not in asset.path)
return read_dandi_file(asset=asset)
Now we start streaming data directly from the DANDI Archive! Instead of downloading each entire file (usually around 500 MB; and there are 1518 files!), streaming simply requests the minimal amount of data needed to perform an immediate operation.
We begin by connecting a client and fetching the dandiset object.
dandiset_id = "000728"
client = DandiAPIClient()
dandiset = client.get_dandiset(dandiset_id=dandiset_id)
Now we can load the NWB file from the dandiset that corresponds to our session ID.
nwbfile = read_visual_coding_ophys_file(dandiset=dandiset, session_id=session_id)
Try exploring the file contents interactively by expanding the dropdowns!
nwbfile
root (NWBFile)
session_start_time
2016-02-04 10:25:24-08:00timestamps_reference_time
2016-02-04 10:25:24-08:00file_create_date
0
2024-03-19 15:55:50.497145-04:00stimulus
natural_movie_one_stimulus
data
timestamps
indexed_timeseries
data
device
natural_scenes_stimulus
data
timestamps
spontaneous_stimulus
table
| start_time | stop_time | |
|---|---|---|
| id | ||
| 0 | 1047.42969 | 1342.66703 |
static_gratings
table
| start_time | stop_time | orientation_in_degrees | spatial_frequency_in_cycles_per_degree | phase | is_blank_sweep | |
|---|---|---|---|---|---|---|
| id | ||||||
| 0 | 51.55381 | 51.80381 | 90.0 | 0.04 | 0.5 | False |
| 1 | 51.78653 | 52.03653 | 150.0 | 0.04 | 0.5 | False |
| 2 | 52.05250 | 52.30250 | 30.0 | 0.02 | 0.0 | False |
stimulus_template
natural_movie_one
data
device
natural_scenes_template
images
NaturalScene0
NaturalScene1
NaturalScene10
NaturalScene100
NaturalScene101
NaturalScene102
NaturalScene103
NaturalScene104
NaturalScene105
NaturalScene106
NaturalScene107
NaturalScene108
NaturalScene109
NaturalScene11
NaturalScene110
NaturalScene111
NaturalScene112
NaturalScene113
NaturalScene114
NaturalScene115
NaturalScene116
NaturalScene117
NaturalScene12
NaturalScene13
NaturalScene14
NaturalScene15
NaturalScene16
NaturalScene17
NaturalScene18
NaturalScene19
NaturalScene2
NaturalScene20
NaturalScene21
NaturalScene22
NaturalScene23
NaturalScene24
NaturalScene25
NaturalScene26
NaturalScene27
NaturalScene28
NaturalScene29
NaturalScene3
NaturalScene30
NaturalScene31
NaturalScene32
NaturalScene33
NaturalScene34
NaturalScene35
NaturalScene36
NaturalScene37
NaturalScene38
NaturalScene39
NaturalScene4
NaturalScene40
NaturalScene41
NaturalScene42
NaturalScene43
NaturalScene44
NaturalScene45
NaturalScene46
NaturalScene47
NaturalScene48
NaturalScene49
NaturalScene5
NaturalScene50
NaturalScene51
NaturalScene52
NaturalScene53
NaturalScene54
NaturalScene55
NaturalScene56
NaturalScene57
NaturalScene58
NaturalScene59
NaturalScene6
NaturalScene60
NaturalScene61
NaturalScene62
NaturalScene63
NaturalScene64
NaturalScene65
NaturalScene66
NaturalScene67
NaturalScene68
NaturalScene69
NaturalScene7
NaturalScene70
NaturalScene71
NaturalScene72
NaturalScene73
NaturalScene74
NaturalScene75
NaturalScene76
NaturalScene77
NaturalScene78
NaturalScene79
NaturalScene8
NaturalScene80
NaturalScene81
NaturalScene82
NaturalScene83
NaturalScene84
NaturalScene85
NaturalScene86
NaturalScene87
NaturalScene88
NaturalScene89
NaturalScene9
NaturalScene90
NaturalScene91
NaturalScene92
NaturalScene93
NaturalScene94
NaturalScene95
NaturalScene96
NaturalScene97
NaturalScene98
NaturalScene99
processing
behavior
data_interfaces
BehavioralTimeSeries
time_series
running_speed
data
timestamps
ophys
data_interfaces
DfOverF
roi_response_series
DfOverF
data
timestamps (link to processing/ophys/Fluorescence/Corrected/timestamps)
rois
table
imaging_plane
optical_channel
0
device
grid_spacing
table
| global_roi_id | pixel_mask | |
|---|---|---|
| id | ||
| 517473350 | 517473350 | [[55, 291, 1.0], [55, 292, 1.0], [56, 290, 1.0], [56, 291, 1.0], [56, 292, 1.0], [56, 293, 1.0], [56, 294, 1.0], [56, 295, 1.0], [56, 296, 1.0], [57, 289, 1.0], [57, 290, 1.0], [57, 291, 1.0], [57, 292, 1.0], [57, 293, 1.0], [57, 294, 1.0], [57, 295, 1.0], [57, 296, 1.0], [57, 297, 1.0], [57, 298, 1.0], [58, 288, 1.0], [58, 289, 1.0], [58, 290, 1.0], [58, 291, 1.0], [58, 292, 1.0], [58, 293, 1.0], [58, 294, 1.0], [58, 295, 1.0], [58, 296, 1.0], [58, 297, 1.0], [58, 298, 1.0], [58, 299, 1.0], [59, 288, 1.0], [59, 289, 1.0], [59, 290, 1.0], [59, 291, 1.0], [59, 292, 1.0], [59, 293, 1.0], [59, 294, 1.0], [59, 295, 1.0], [59, 296, 1.0], [59, 297, 1.0], [59, 298, 1.0], [59, 299, 1.0], [60, 288, 1.0], [60, 289, 1.0], [60, 290, 1.0], [60, 291, 1.0], [60, 292, 1.0], [60, 293, 1.0], [60, 294, 1.0], [60, 295, 1.0], [60, 296, 1.0], [60, 297, 1.0], [60, 298, 1.0], [60, 299, 1.0], [61, 287, 1.0], [61, 288, 1.0], [61, 289, 1.0], [61, 290, 1.0], [61, 291, 1.0], [61, 292, 1.0], [61, 293, 1.0], [61, 294, 1.0], [61, 295, 1.0], [61, 296, 1.0], [61, 297, 1.0], [61, 298, 1.0], [61, 299, 1.0], [62, 287, 1.0], [62, 288, 1.0], [62, 289, 1.0], [62, 290, 1.0], [62, 291, 1.0], [62, 292, 1.0], [62, 293, 1.0], [62, 294, 1.0], [62, 295, 1.0], [62, 296, 1.0], [62, 297, 1.0], [62, 298, 1.0], [62, 299, 1.0], [63, 287, 1.0], [63, 288, 1.0], [63, 289, 1.0], [63, 290, 1.0], [63, 291, 1.0], [63, 292, 1.0], [63, 293, 1.0], [63, 294, 1.0], [63, 295, 1.0], [63, 296, 1.0], [63, 297, 1.0], [63, 298, 1.0], [63, 299, 1.0], [64, 287, 1.0], [64, 288, 1.0], [64, 289, 1.0], [64, 290, 1.0], [64, 291, 1.0], [64, 292, 1.0], ...] |
| 517473341 | 517473341 | [[58, 272, 1.0], [58, 273, 1.0], [58, 274, 1.0], [58, 275, 1.0], [58, 276, 1.0], [58, 277, 1.0], [59, 271, 1.0], [59, 272, 1.0], [59, 273, 1.0], [59, 274, 1.0], [59, 275, 1.0], [59, 276, 1.0], [59, 277, 1.0], [59, 278, 1.0], [59, 279, 1.0], [59, 280, 1.0], [59, 281, 1.0], [59, 282, 1.0], [60, 271, 1.0], [60, 272, 1.0], [60, 273, 1.0], [60, 274, 1.0], [60, 275, 1.0], [60, 276, 1.0], [60, 277, 1.0], [60, 278, 1.0], [60, 279, 1.0], [60, 280, 1.0], [60, 281, 1.0], [60, 282, 1.0], [60, 283, 1.0], [61, 270, 1.0], [61, 271, 1.0], [61, 272, 1.0], [61, 273, 1.0], [61, 274, 1.0], [61, 275, 1.0], [61, 276, 1.0], [61, 277, 1.0], [61, 278, 1.0], [61, 279, 1.0], [61, 280, 1.0], [61, 281, 1.0], [61, 282, 1.0], [62, 270, 1.0], [62, 271, 1.0], [62, 272, 1.0], [62, 273, 1.0], [62, 274, 1.0], [62, 275, 1.0], [62, 276, 1.0], [62, 277, 1.0], [62, 278, 1.0], [62, 279, 1.0], [62, 280, 1.0], [62, 281, 1.0], [62, 282, 1.0], [63, 269, 1.0], [63, 270, 1.0], [63, 271, 1.0], [63, 272, 1.0], [63, 273, 1.0], [63, 274, 1.0], [63, 275, 1.0], [63, 276, 1.0], [63, 277, 1.0], [63, 278, 1.0], [63, 279, 1.0], [63, 280, 1.0], [63, 281, 1.0], [63, 282, 1.0], [64, 268, 1.0], [64, 269, 1.0], [64, 270, 1.0], [64, 271, 1.0], [64, 272, 1.0], [64, 273, 1.0], [64, 274, 1.0], [64, 275, 1.0], [64, 276, 1.0], [64, 277, 1.0], [64, 278, 1.0], [64, 279, 1.0], [64, 280, 1.0], [64, 281, 1.0], [64, 282, 1.0], [65, 268, 1.0], [65, 269, 1.0], [65, 270, 1.0], [65, 271, 1.0], [65, 272, 1.0], [65, 273, 1.0], [65, 274, 1.0], [65, 275, 1.0], [65, 276, 1.0], [65, 277, 1.0], [65, 278, 1.0], [65, 279, 1.0], [65, 280, 1.0], [65, 281, 1.0], ...] |
| 517473313 | 517473313 | [[33, 459, 1.0], [33, 460, 1.0], [33, 461, 1.0], [33, 462, 1.0], [33, 463, 1.0], [34, 458, 1.0], [34, 459, 1.0], [34, 460, 1.0], [34, 461, 1.0], [34, 462, 1.0], [34, 463, 1.0], [34, 464, 1.0], [35, 457, 1.0], [35, 458, 1.0], [35, 459, 1.0], [35, 460, 1.0], [35, 461, 1.0], [35, 462, 1.0], [35, 463, 1.0], [35, 464, 1.0], [35, 465, 1.0], [35, 466, 1.0], [36, 456, 1.0], [36, 457, 1.0], [36, 458, 1.0], [36, 459, 1.0], [36, 460, 1.0], [36, 461, 1.0], [36, 462, 1.0], [36, 463, 1.0], [36, 464, 1.0], [36, 465, 1.0], [36, 466, 1.0], [36, 467, 1.0], [37, 456, 1.0], [37, 457, 1.0], [37, 458, 1.0], [37, 459, 1.0], [37, 460, 1.0], [37, 461, 1.0], [37, 462, 1.0], [37, 463, 1.0], [37, 464, 1.0], [37, 465, 1.0], [37, 466, 1.0], [37, 467, 1.0], [38, 455, 1.0], [38, 456, 1.0], [38, 457, 1.0], [38, 458, 1.0], [38, 459, 1.0], [38, 460, 1.0], [38, 461, 1.0], [38, 462, 1.0], [38, 463, 1.0], [38, 464, 1.0], [38, 465, 1.0], [38, 466, 1.0], [38, 467, 1.0], [39, 455, 1.0], [39, 456, 1.0], [39, 457, 1.0], [39, 458, 1.0], [39, 459, 1.0], [39, 460, 1.0], [39, 461, 1.0], [39, 462, 1.0], [39, 463, 1.0], [39, 464, 1.0], [39, 465, 1.0], [39, 466, 1.0], [39, 467, 1.0], [40, 455, 1.0], [40, 456, 1.0], [40, 457, 1.0], [40, 458, 1.0], [40, 459, 1.0], [40, 460, 1.0], [40, 461, 1.0], [40, 462, 1.0], [40, 463, 1.0], [40, 464, 1.0], [40, 465, 1.0], [40, 466, 1.0], [41, 455, 1.0], [41, 456, 1.0], [41, 457, 1.0], [41, 458, 1.0], [41, 459, 1.0], [41, 460, 1.0], [41, 461, 1.0], [41, 462, 1.0], [41, 463, 1.0], [41, 464, 1.0], [41, 465, 1.0], [41, 466, 1.0], [42, 455, 1.0], [42, 456, 1.0], [42, 457, 1.0], [42, 458, 1.0], ...] |
| 517473255 | 517473255 | [[41, 493, 1.0], [41, 494, 1.0], [41, 495, 1.0], [41, 496, 1.0], [42, 492, 1.0], [42, 493, 1.0], [42, 494, 1.0], [42, 495, 1.0], [42, 496, 1.0], [42, 497, 1.0], [43, 489, 1.0], [43, 490, 1.0], [43, 491, 1.0], [43, 492, 1.0], [43, 493, 1.0], [43, 494, 1.0], [43, 495, 1.0], [43, 496, 1.0], [43, 497, 1.0], [43, 498, 1.0], [44, 488, 1.0], [44, 489, 1.0], [44, 490, 1.0], [44, 491, 1.0], [44, 492, 1.0], [44, 493, 1.0], [44, 494, 1.0], [44, 495, 1.0], [44, 496, 1.0], [44, 497, 1.0], [44, 498, 1.0], [45, 487, 1.0], [45, 488, 1.0], [45, 489, 1.0], [45, 490, 1.0], [45, 491, 1.0], [45, 492, 1.0], [45, 493, 1.0], [45, 494, 1.0], [45, 495, 1.0], [45, 496, 1.0], [45, 497, 1.0], [45, 498, 1.0], [46, 486, 1.0], [46, 487, 1.0], [46, 488, 1.0], [46, 489, 1.0], [46, 490, 1.0], [46, 491, 1.0], [46, 492, 1.0], [46, 493, 1.0], [46, 494, 1.0], [46, 495, 1.0], [46, 496, 1.0], [46, 497, 1.0], [46, 498, 1.0], [47, 486, 1.0], [47, 487, 1.0], [47, 488, 1.0], [47, 489, 1.0], [47, 490, 1.0], [47, 491, 1.0], [47, 492, 1.0], [47, 493, 1.0], [47, 494, 1.0], [47, 495, 1.0], [47, 496, 1.0], [47, 497, 1.0], [47, 498, 1.0], [47, 499, 1.0], [48, 486, 1.0], [48, 487, 1.0], [48, 488, 1.0], [48, 489, 1.0], [48, 490, 1.0], [48, 491, 1.0], [48, 492, 1.0], [48, 493, 1.0], [48, 494, 1.0], [48, 495, 1.0], [48, 496, 1.0], [48, 497, 1.0], [48, 498, 1.0], [48, 499, 1.0], [49, 486, 1.0], [49, 487, 1.0], [49, 488, 1.0], [49, 489, 1.0], [49, 490, 1.0], [49, 491, 1.0], [49, 492, 1.0], [49, 493, 1.0], [49, 494, 1.0], [49, 495, 1.0], [49, 496, 1.0], [49, 497, 1.0], [49, 498, 1.0], [49, 499, 1.0], [50, 486, 1.0], [50, 487, 1.0], ...] |
... and 170 more rows.
DfOverFEvents
data
timestamps (link to processing/ophys/Fluorescence/Corrected/timestamps)
rois
table
imaging_plane
optical_channel
0
device
grid_spacing
table
| global_roi_id | pixel_mask | |
|---|---|---|
| id | ||
| 517473350 | 517473350 | [[55, 291, 1.0], [55, 292, 1.0], [56, 290, 1.0], [56, 291, 1.0], [56, 292, 1.0], [56, 293, 1.0], [56, 294, 1.0], [56, 295, 1.0], [56, 296, 1.0], [57, 289, 1.0], [57, 290, 1.0], [57, 291, 1.0], [57, 292, 1.0], [57, 293, 1.0], [57, 294, 1.0], [57, 295, 1.0], [57, 296, 1.0], [57, 297, 1.0], [57, 298, 1.0], [58, 288, 1.0], [58, 289, 1.0], [58, 290, 1.0], [58, 291, 1.0], [58, 292, 1.0], [58, 293, 1.0], [58, 294, 1.0], [58, 295, 1.0], [58, 296, 1.0], [58, 297, 1.0], [58, 298, 1.0], [58, 299, 1.0], [59, 288, 1.0], [59, 289, 1.0], [59, 290, 1.0], [59, 291, 1.0], [59, 292, 1.0], [59, 293, 1.0], [59, 294, 1.0], [59, 295, 1.0], [59, 296, 1.0], [59, 297, 1.0], [59, 298, 1.0], [59, 299, 1.0], [60, 288, 1.0], [60, 289, 1.0], [60, 290, 1.0], [60, 291, 1.0], [60, 292, 1.0], [60, 293, 1.0], [60, 294, 1.0], [60, 295, 1.0], [60, 296, 1.0], [60, 297, 1.0], [60, 298, 1.0], [60, 299, 1.0], [61, 287, 1.0], [61, 288, 1.0], [61, 289, 1.0], [61, 290, 1.0], [61, 291, 1.0], [61, 292, 1.0], [61, 293, 1.0], [61, 294, 1.0], [61, 295, 1.0], [61, 296, 1.0], [61, 297, 1.0], [61, 298, 1.0], [61, 299, 1.0], [62, 287, 1.0], [62, 288, 1.0], [62, 289, 1.0], [62, 290, 1.0], [62, 291, 1.0], [62, 292, 1.0], [62, 293, 1.0], [62, 294, 1.0], [62, 295, 1.0], [62, 296, 1.0], [62, 297, 1.0], [62, 298, 1.0], [62, 299, 1.0], [63, 287, 1.0], [63, 288, 1.0], [63, 289, 1.0], [63, 290, 1.0], [63, 291, 1.0], [63, 292, 1.0], [63, 293, 1.0], [63, 294, 1.0], [63, 295, 1.0], [63, 296, 1.0], [63, 297, 1.0], [63, 298, 1.0], [63, 299, 1.0], [64, 287, 1.0], [64, 288, 1.0], [64, 289, 1.0], [64, 290, 1.0], [64, 291, 1.0], [64, 292, 1.0], ...] |
| 517473341 | 517473341 | [[58, 272, 1.0], [58, 273, 1.0], [58, 274, 1.0], [58, 275, 1.0], [58, 276, 1.0], [58, 277, 1.0], [59, 271, 1.0], [59, 272, 1.0], [59, 273, 1.0], [59, 274, 1.0], [59, 275, 1.0], [59, 276, 1.0], [59, 277, 1.0], [59, 278, 1.0], [59, 279, 1.0], [59, 280, 1.0], [59, 281, 1.0], [59, 282, 1.0], [60, 271, 1.0], [60, 272, 1.0], [60, 273, 1.0], [60, 274, 1.0], [60, 275, 1.0], [60, 276, 1.0], [60, 277, 1.0], [60, 278, 1.0], [60, 279, 1.0], [60, 280, 1.0], [60, 281, 1.0], [60, 282, 1.0], [60, 283, 1.0], [61, 270, 1.0], [61, 271, 1.0], [61, 272, 1.0], [61, 273, 1.0], [61, 274, 1.0], [61, 275, 1.0], [61, 276, 1.0], [61, 277, 1.0], [61, 278, 1.0], [61, 279, 1.0], [61, 280, 1.0], [61, 281, 1.0], [61, 282, 1.0], [62, 270, 1.0], [62, 271, 1.0], [62, 272, 1.0], [62, 273, 1.0], [62, 274, 1.0], [62, 275, 1.0], [62, 276, 1.0], [62, 277, 1.0], [62, 278, 1.0], [62, 279, 1.0], [62, 280, 1.0], [62, 281, 1.0], [62, 282, 1.0], [63, 269, 1.0], [63, 270, 1.0], [63, 271, 1.0], [63, 272, 1.0], [63, 273, 1.0], [63, 274, 1.0], [63, 275, 1.0], [63, 276, 1.0], [63, 277, 1.0], [63, 278, 1.0], [63, 279, 1.0], [63, 280, 1.0], [63, 281, 1.0], [63, 282, 1.0], [64, 268, 1.0], [64, 269, 1.0], [64, 270, 1.0], [64, 271, 1.0], [64, 272, 1.0], [64, 273, 1.0], [64, 274, 1.0], [64, 275, 1.0], [64, 276, 1.0], [64, 277, 1.0], [64, 278, 1.0], [64, 279, 1.0], [64, 280, 1.0], [64, 281, 1.0], [64, 282, 1.0], [65, 268, 1.0], [65, 269, 1.0], [65, 270, 1.0], [65, 271, 1.0], [65, 272, 1.0], [65, 273, 1.0], [65, 274, 1.0], [65, 275, 1.0], [65, 276, 1.0], [65, 277, 1.0], [65, 278, 1.0], [65, 279, 1.0], [65, 280, 1.0], [65, 281, 1.0], ...] |
| 517473313 | 517473313 | [[33, 459, 1.0], [33, 460, 1.0], [33, 461, 1.0], [33, 462, 1.0], [33, 463, 1.0], [34, 458, 1.0], [34, 459, 1.0], [34, 460, 1.0], [34, 461, 1.0], [34, 462, 1.0], [34, 463, 1.0], [34, 464, 1.0], [35, 457, 1.0], [35, 458, 1.0], [35, 459, 1.0], [35, 460, 1.0], [35, 461, 1.0], [35, 462, 1.0], [35, 463, 1.0], [35, 464, 1.0], [35, 465, 1.0], [35, 466, 1.0], [36, 456, 1.0], [36, 457, 1.0], [36, 458, 1.0], [36, 459, 1.0], [36, 460, 1.0], [36, 461, 1.0], [36, 462, 1.0], [36, 463, 1.0], [36, 464, 1.0], [36, 465, 1.0], [36, 466, 1.0], [36, 467, 1.0], [37, 456, 1.0], [37, 457, 1.0], [37, 458, 1.0], [37, 459, 1.0], [37, 460, 1.0], [37, 461, 1.0], [37, 462, 1.0], [37, 463, 1.0], [37, 464, 1.0], [37, 465, 1.0], [37, 466, 1.0], [37, 467, 1.0], [38, 455, 1.0], [38, 456, 1.0], [38, 457, 1.0], [38, 458, 1.0], [38, 459, 1.0], [38, 460, 1.0], [38, 461, 1.0], [38, 462, 1.0], [38, 463, 1.0], [38, 464, 1.0], [38, 465, 1.0], [38, 466, 1.0], [38, 467, 1.0], [39, 455, 1.0], [39, 456, 1.0], [39, 457, 1.0], [39, 458, 1.0], [39, 459, 1.0], [39, 460, 1.0], [39, 461, 1.0], [39, 462, 1.0], [39, 463, 1.0], [39, 464, 1.0], [39, 465, 1.0], [39, 466, 1.0], [39, 467, 1.0], [40, 455, 1.0], [40, 456, 1.0], [40, 457, 1.0], [40, 458, 1.0], [40, 459, 1.0], [40, 460, 1.0], [40, 461, 1.0], [40, 462, 1.0], [40, 463, 1.0], [40, 464, 1.0], [40, 465, 1.0], [40, 466, 1.0], [41, 455, 1.0], [41, 456, 1.0], [41, 457, 1.0], [41, 458, 1.0], [41, 459, 1.0], [41, 460, 1.0], [41, 461, 1.0], [41, 462, 1.0], [41, 463, 1.0], [41, 464, 1.0], [41, 465, 1.0], [41, 466, 1.0], [42, 455, 1.0], [42, 456, 1.0], [42, 457, 1.0], [42, 458, 1.0], ...] |
| 517473255 | 517473255 | [[41, 493, 1.0], [41, 494, 1.0], [41, 495, 1.0], [41, 496, 1.0], [42, 492, 1.0], [42, 493, 1.0], [42, 494, 1.0], [42, 495, 1.0], [42, 496, 1.0], [42, 497, 1.0], [43, 489, 1.0], [43, 490, 1.0], [43, 491, 1.0], [43, 492, 1.0], [43, 493, 1.0], [43, 494, 1.0], [43, 495, 1.0], [43, 496, 1.0], [43, 497, 1.0], [43, 498, 1.0], [44, 488, 1.0], [44, 489, 1.0], [44, 490, 1.0], [44, 491, 1.0], [44, 492, 1.0], [44, 493, 1.0], [44, 494, 1.0], [44, 495, 1.0], [44, 496, 1.0], [44, 497, 1.0], [44, 498, 1.0], [45, 487, 1.0], [45, 488, 1.0], [45, 489, 1.0], [45, 490, 1.0], [45, 491, 1.0], [45, 492, 1.0], [45, 493, 1.0], [45, 494, 1.0], [45, 495, 1.0], [45, 496, 1.0], [45, 497, 1.0], [45, 498, 1.0], [46, 486, 1.0], [46, 487, 1.0], [46, 488, 1.0], [46, 489, 1.0], [46, 490, 1.0], [46, 491, 1.0], [46, 492, 1.0], [46, 493, 1.0], [46, 494, 1.0], [46, 495, 1.0], [46, 496, 1.0], [46, 497, 1.0], [46, 498, 1.0], [47, 486, 1.0], [47, 487, 1.0], [47, 488, 1.0], [47, 489, 1.0], [47, 490, 1.0], [47, 491, 1.0], [47, 492, 1.0], [47, 493, 1.0], [47, 494, 1.0], [47, 495, 1.0], [47, 496, 1.0], [47, 497, 1.0], [47, 498, 1.0], [47, 499, 1.0], [48, 486, 1.0], [48, 487, 1.0], [48, 488, 1.0], [48, 489, 1.0], [48, 490, 1.0], [48, 491, 1.0], [48, 492, 1.0], [48, 493, 1.0], [48, 494, 1.0], [48, 495, 1.0], [48, 496, 1.0], [48, 497, 1.0], [48, 498, 1.0], [48, 499, 1.0], [49, 486, 1.0], [49, 487, 1.0], [49, 488, 1.0], [49, 489, 1.0], [49, 490, 1.0], [49, 491, 1.0], [49, 492, 1.0], [49, 493, 1.0], [49, 494, 1.0], [49, 495, 1.0], [49, 496, 1.0], [49, 497, 1.0], [49, 498, 1.0], [49, 499, 1.0], [50, 486, 1.0], [50, 487, 1.0], ...] |
... and 170 more rows.
Fluorescence
roi_response_series
Corrected
data
timestamps
rois
table
imaging_plane
optical_channel
0
device
grid_spacing
table
| global_roi_id | pixel_mask | |
|---|---|---|
| id | ||
| 517473350 | 517473350 | [[55, 291, 1.0], [55, 292, 1.0], [56, 290, 1.0], [56, 291, 1.0], [56, 292, 1.0], [56, 293, 1.0], [56, 294, 1.0], [56, 295, 1.0], [56, 296, 1.0], [57, 289, 1.0], [57, 290, 1.0], [57, 291, 1.0], [57, 292, 1.0], [57, 293, 1.0], [57, 294, 1.0], [57, 295, 1.0], [57, 296, 1.0], [57, 297, 1.0], [57, 298, 1.0], [58, 288, 1.0], [58, 289, 1.0], [58, 290, 1.0], [58, 291, 1.0], [58, 292, 1.0], [58, 293, 1.0], [58, 294, 1.0], [58, 295, 1.0], [58, 296, 1.0], [58, 297, 1.0], [58, 298, 1.0], [58, 299, 1.0], [59, 288, 1.0], [59, 289, 1.0], [59, 290, 1.0], [59, 291, 1.0], [59, 292, 1.0], [59, 293, 1.0], [59, 294, 1.0], [59, 295, 1.0], [59, 296, 1.0], [59, 297, 1.0], [59, 298, 1.0], [59, 299, 1.0], [60, 288, 1.0], [60, 289, 1.0], [60, 290, 1.0], [60, 291, 1.0], [60, 292, 1.0], [60, 293, 1.0], [60, 294, 1.0], [60, 295, 1.0], [60, 296, 1.0], [60, 297, 1.0], [60, 298, 1.0], [60, 299, 1.0], [61, 287, 1.0], [61, 288, 1.0], [61, 289, 1.0], [61, 290, 1.0], [61, 291, 1.0], [61, 292, 1.0], [61, 293, 1.0], [61, 294, 1.0], [61, 295, 1.0], [61, 296, 1.0], [61, 297, 1.0], [61, 298, 1.0], [61, 299, 1.0], [62, 287, 1.0], [62, 288, 1.0], [62, 289, 1.0], [62, 290, 1.0], [62, 291, 1.0], [62, 292, 1.0], [62, 293, 1.0], [62, 294, 1.0], [62, 295, 1.0], [62, 296, 1.0], [62, 297, 1.0], [62, 298, 1.0], [62, 299, 1.0], [63, 287, 1.0], [63, 288, 1.0], [63, 289, 1.0], [63, 290, 1.0], [63, 291, 1.0], [63, 292, 1.0], [63, 293, 1.0], [63, 294, 1.0], [63, 295, 1.0], [63, 296, 1.0], [63, 297, 1.0], [63, 298, 1.0], [63, 299, 1.0], [64, 287, 1.0], [64, 288, 1.0], [64, 289, 1.0], [64, 290, 1.0], [64, 291, 1.0], [64, 292, 1.0], ...] |
| 517473341 | 517473341 | [[58, 272, 1.0], [58, 273, 1.0], [58, 274, 1.0], [58, 275, 1.0], [58, 276, 1.0], [58, 277, 1.0], [59, 271, 1.0], [59, 272, 1.0], [59, 273, 1.0], [59, 274, 1.0], [59, 275, 1.0], [59, 276, 1.0], [59, 277, 1.0], [59, 278, 1.0], [59, 279, 1.0], [59, 280, 1.0], [59, 281, 1.0], [59, 282, 1.0], [60, 271, 1.0], [60, 272, 1.0], [60, 273, 1.0], [60, 274, 1.0], [60, 275, 1.0], [60, 276, 1.0], [60, 277, 1.0], [60, 278, 1.0], [60, 279, 1.0], [60, 280, 1.0], [60, 281, 1.0], [60, 282, 1.0], [60, 283, 1.0], [61, 270, 1.0], [61, 271, 1.0], [61, 272, 1.0], [61, 273, 1.0], [61, 274, 1.0], [61, 275, 1.0], [61, 276, 1.0], [61, 277, 1.0], [61, 278, 1.0], [61, 279, 1.0], [61, 280, 1.0], [61, 281, 1.0], [61, 282, 1.0], [62, 270, 1.0], [62, 271, 1.0], [62, 272, 1.0], [62, 273, 1.0], [62, 274, 1.0], [62, 275, 1.0], [62, 276, 1.0], [62, 277, 1.0], [62, 278, 1.0], [62, 279, 1.0], [62, 280, 1.0], [62, 281, 1.0], [62, 282, 1.0], [63, 269, 1.0], [63, 270, 1.0], [63, 271, 1.0], [63, 272, 1.0], [63, 273, 1.0], [63, 274, 1.0], [63, 275, 1.0], [63, 276, 1.0], [63, 277, 1.0], [63, 278, 1.0], [63, 279, 1.0], [63, 280, 1.0], [63, 281, 1.0], [63, 282, 1.0], [64, 268, 1.0], [64, 269, 1.0], [64, 270, 1.0], [64, 271, 1.0], [64, 272, 1.0], [64, 273, 1.0], [64, 274, 1.0], [64, 275, 1.0], [64, 276, 1.0], [64, 277, 1.0], [64, 278, 1.0], [64, 279, 1.0], [64, 280, 1.0], [64, 281, 1.0], [64, 282, 1.0], [65, 268, 1.0], [65, 269, 1.0], [65, 270, 1.0], [65, 271, 1.0], [65, 272, 1.0], [65, 273, 1.0], [65, 274, 1.0], [65, 275, 1.0], [65, 276, 1.0], [65, 277, 1.0], [65, 278, 1.0], [65, 279, 1.0], [65, 280, 1.0], [65, 281, 1.0], ...] |
| 517473313 | 517473313 | [[33, 459, 1.0], [33, 460, 1.0], [33, 461, 1.0], [33, 462, 1.0], [33, 463, 1.0], [34, 458, 1.0], [34, 459, 1.0], [34, 460, 1.0], [34, 461, 1.0], [34, 462, 1.0], [34, 463, 1.0], [34, 464, 1.0], [35, 457, 1.0], [35, 458, 1.0], [35, 459, 1.0], [35, 460, 1.0], [35, 461, 1.0], [35, 462, 1.0], [35, 463, 1.0], [35, 464, 1.0], [35, 465, 1.0], [35, 466, 1.0], [36, 456, 1.0], [36, 457, 1.0], [36, 458, 1.0], [36, 459, 1.0], [36, 460, 1.0], [36, 461, 1.0], [36, 462, 1.0], [36, 463, 1.0], [36, 464, 1.0], [36, 465, 1.0], [36, 466, 1.0], [36, 467, 1.0], [37, 456, 1.0], [37, 457, 1.0], [37, 458, 1.0], [37, 459, 1.0], [37, 460, 1.0], [37, 461, 1.0], [37, 462, 1.0], [37, 463, 1.0], [37, 464, 1.0], [37, 465, 1.0], [37, 466, 1.0], [37, 467, 1.0], [38, 455, 1.0], [38, 456, 1.0], [38, 457, 1.0], [38, 458, 1.0], [38, 459, 1.0], [38, 460, 1.0], [38, 461, 1.0], [38, 462, 1.0], [38, 463, 1.0], [38, 464, 1.0], [38, 465, 1.0], [38, 466, 1.0], [38, 467, 1.0], [39, 455, 1.0], [39, 456, 1.0], [39, 457, 1.0], [39, 458, 1.0], [39, 459, 1.0], [39, 460, 1.0], [39, 461, 1.0], [39, 462, 1.0], [39, 463, 1.0], [39, 464, 1.0], [39, 465, 1.0], [39, 466, 1.0], [39, 467, 1.0], [40, 455, 1.0], [40, 456, 1.0], [40, 457, 1.0], [40, 458, 1.0], [40, 459, 1.0], [40, 460, 1.0], [40, 461, 1.0], [40, 462, 1.0], [40, 463, 1.0], [40, 464, 1.0], [40, 465, 1.0], [40, 466, 1.0], [41, 455, 1.0], [41, 456, 1.0], [41, 457, 1.0], [41, 458, 1.0], [41, 459, 1.0], [41, 460, 1.0], [41, 461, 1.0], [41, 462, 1.0], [41, 463, 1.0], [41, 464, 1.0], [41, 465, 1.0], [41, 466, 1.0], [42, 455, 1.0], [42, 456, 1.0], [42, 457, 1.0], [42, 458, 1.0], ...] |
| 517473255 | 517473255 | [[41, 493, 1.0], [41, 494, 1.0], [41, 495, 1.0], [41, 496, 1.0], [42, 492, 1.0], [42, 493, 1.0], [42, 494, 1.0], [42, 495, 1.0], [42, 496, 1.0], [42, 497, 1.0], [43, 489, 1.0], [43, 490, 1.0], [43, 491, 1.0], [43, 492, 1.0], [43, 493, 1.0], [43, 494, 1.0], [43, 495, 1.0], [43, 496, 1.0], [43, 497, 1.0], [43, 498, 1.0], [44, 488, 1.0], [44, 489, 1.0], [44, 490, 1.0], [44, 491, 1.0], [44, 492, 1.0], [44, 493, 1.0], [44, 494, 1.0], [44, 495, 1.0], [44, 496, 1.0], [44, 497, 1.0], [44, 498, 1.0], [45, 487, 1.0], [45, 488, 1.0], [45, 489, 1.0], [45, 490, 1.0], [45, 491, 1.0], [45, 492, 1.0], [45, 493, 1.0], [45, 494, 1.0], [45, 495, 1.0], [45, 496, 1.0], [45, 497, 1.0], [45, 498, 1.0], [46, 486, 1.0], [46, 487, 1.0], [46, 488, 1.0], [46, 489, 1.0], [46, 490, 1.0], [46, 491, 1.0], [46, 492, 1.0], [46, 493, 1.0], [46, 494, 1.0], [46, 495, 1.0], [46, 496, 1.0], [46, 497, 1.0], [46, 498, 1.0], [47, 486, 1.0], [47, 487, 1.0], [47, 488, 1.0], [47, 489, 1.0], [47, 490, 1.0], [47, 491, 1.0], [47, 492, 1.0], [47, 493, 1.0], [47, 494, 1.0], [47, 495, 1.0], [47, 496, 1.0], [47, 497, 1.0], [47, 498, 1.0], [47, 499, 1.0], [48, 486, 1.0], [48, 487, 1.0], [48, 488, 1.0], [48, 489, 1.0], [48, 490, 1.0], [48, 491, 1.0], [48, 492, 1.0], [48, 493, 1.0], [48, 494, 1.0], [48, 495, 1.0], [48, 496, 1.0], [48, 497, 1.0], [48, 498, 1.0], [48, 499, 1.0], [49, 486, 1.0], [49, 487, 1.0], [49, 488, 1.0], [49, 489, 1.0], [49, 490, 1.0], [49, 491, 1.0], [49, 492, 1.0], [49, 493, 1.0], [49, 494, 1.0], [49, 495, 1.0], [49, 496, 1.0], [49, 497, 1.0], [49, 498, 1.0], [49, 499, 1.0], [50, 486, 1.0], [50, 487, 1.0], ...] |
... and 170 more rows.
timestamp_link
Demixed
data
timestamps (link to processing/ophys/Fluorescence/Corrected/timestamps)
rois
table
imaging_plane
optical_channel
0
device
grid_spacing
table
| global_roi_id | pixel_mask | |
|---|---|---|
| id | ||
| 517473350 | 517473350 | [[55, 291, 1.0], [55, 292, 1.0], [56, 290, 1.0], [56, 291, 1.0], [56, 292, 1.0], [56, 293, 1.0], [56, 294, 1.0], [56, 295, 1.0], [56, 296, 1.0], [57, 289, 1.0], [57, 290, 1.0], [57, 291, 1.0], [57, 292, 1.0], [57, 293, 1.0], [57, 294, 1.0], [57, 295, 1.0], [57, 296, 1.0], [57, 297, 1.0], [57, 298, 1.0], [58, 288, 1.0], [58, 289, 1.0], [58, 290, 1.0], [58, 291, 1.0], [58, 292, 1.0], [58, 293, 1.0], [58, 294, 1.0], [58, 295, 1.0], [58, 296, 1.0], [58, 297, 1.0], [58, 298, 1.0], [58, 299, 1.0], [59, 288, 1.0], [59, 289, 1.0], [59, 290, 1.0], [59, 291, 1.0], [59, 292, 1.0], [59, 293, 1.0], [59, 294, 1.0], [59, 295, 1.0], [59, 296, 1.0], [59, 297, 1.0], [59, 298, 1.0], [59, 299, 1.0], [60, 288, 1.0], [60, 289, 1.0], [60, 290, 1.0], [60, 291, 1.0], [60, 292, 1.0], [60, 293, 1.0], [60, 294, 1.0], [60, 295, 1.0], [60, 296, 1.0], [60, 297, 1.0], [60, 298, 1.0], [60, 299, 1.0], [61, 287, 1.0], [61, 288, 1.0], [61, 289, 1.0], [61, 290, 1.0], [61, 291, 1.0], [61, 292, 1.0], [61, 293, 1.0], [61, 294, 1.0], [61, 295, 1.0], [61, 296, 1.0], [61, 297, 1.0], [61, 298, 1.0], [61, 299, 1.0], [62, 287, 1.0], [62, 288, 1.0], [62, 289, 1.0], [62, 290, 1.0], [62, 291, 1.0], [62, 292, 1.0], [62, 293, 1.0], [62, 294, 1.0], [62, 295, 1.0], [62, 296, 1.0], [62, 297, 1.0], [62, 298, 1.0], [62, 299, 1.0], [63, 287, 1.0], [63, 288, 1.0], [63, 289, 1.0], [63, 290, 1.0], [63, 291, 1.0], [63, 292, 1.0], [63, 293, 1.0], [63, 294, 1.0], [63, 295, 1.0], [63, 296, 1.0], [63, 297, 1.0], [63, 298, 1.0], [63, 299, 1.0], [64, 287, 1.0], [64, 288, 1.0], [64, 289, 1.0], [64, 290, 1.0], [64, 291, 1.0], [64, 292, 1.0], ...] |
| 517473341 | 517473341 | [[58, 272, 1.0], [58, 273, 1.0], [58, 274, 1.0], [58, 275, 1.0], [58, 276, 1.0], [58, 277, 1.0], [59, 271, 1.0], [59, 272, 1.0], [59, 273, 1.0], [59, 274, 1.0], [59, 275, 1.0], [59, 276, 1.0], [59, 277, 1.0], [59, 278, 1.0], [59, 279, 1.0], [59, 280, 1.0], [59, 281, 1.0], [59, 282, 1.0], [60, 271, 1.0], [60, 272, 1.0], [60, 273, 1.0], [60, 274, 1.0], [60, 275, 1.0], [60, 276, 1.0], [60, 277, 1.0], [60, 278, 1.0], [60, 279, 1.0], [60, 280, 1.0], [60, 281, 1.0], [60, 282, 1.0], [60, 283, 1.0], [61, 270, 1.0], [61, 271, 1.0], [61, 272, 1.0], [61, 273, 1.0], [61, 274, 1.0], [61, 275, 1.0], [61, 276, 1.0], [61, 277, 1.0], [61, 278, 1.0], [61, 279, 1.0], [61, 280, 1.0], [61, 281, 1.0], [61, 282, 1.0], [62, 270, 1.0], [62, 271, 1.0], [62, 272, 1.0], [62, 273, 1.0], [62, 274, 1.0], [62, 275, 1.0], [62, 276, 1.0], [62, 277, 1.0], [62, 278, 1.0], [62, 279, 1.0], [62, 280, 1.0], [62, 281, 1.0], [62, 282, 1.0], [63, 269, 1.0], [63, 270, 1.0], [63, 271, 1.0], [63, 272, 1.0], [63, 273, 1.0], [63, 274, 1.0], [63, 275, 1.0], [63, 276, 1.0], [63, 277, 1.0], [63, 278, 1.0], [63, 279, 1.0], [63, 280, 1.0], [63, 281, 1.0], [63, 282, 1.0], [64, 268, 1.0], [64, 269, 1.0], [64, 270, 1.0], [64, 271, 1.0], [64, 272, 1.0], [64, 273, 1.0], [64, 274, 1.0], [64, 275, 1.0], [64, 276, 1.0], [64, 277, 1.0], [64, 278, 1.0], [64, 279, 1.0], [64, 280, 1.0], [64, 281, 1.0], [64, 282, 1.0], [65, 268, 1.0], [65, 269, 1.0], [65, 270, 1.0], [65, 271, 1.0], [65, 272, 1.0], [65, 273, 1.0], [65, 274, 1.0], [65, 275, 1.0], [65, 276, 1.0], [65, 277, 1.0], [65, 278, 1.0], [65, 279, 1.0], [65, 280, 1.0], [65, 281, 1.0], ...] |
| 517473313 | 517473313 | [[33, 459, 1.0], [33, 460, 1.0], [33, 461, 1.0], [33, 462, 1.0], [33, 463, 1.0], [34, 458, 1.0], [34, 459, 1.0], [34, 460, 1.0], [34, 461, 1.0], [34, 462, 1.0], [34, 463, 1.0], [34, 464, 1.0], [35, 457, 1.0], [35, 458, 1.0], [35, 459, 1.0], [35, 460, 1.0], [35, 461, 1.0], [35, 462, 1.0], [35, 463, 1.0], [35, 464, 1.0], [35, 465, 1.0], [35, 466, 1.0], [36, 456, 1.0], [36, 457, 1.0], [36, 458, 1.0], [36, 459, 1.0], [36, 460, 1.0], [36, 461, 1.0], [36, 462, 1.0], [36, 463, 1.0], [36, 464, 1.0], [36, 465, 1.0], [36, 466, 1.0], [36, 467, 1.0], [37, 456, 1.0], [37, 457, 1.0], [37, 458, 1.0], [37, 459, 1.0], [37, 460, 1.0], [37, 461, 1.0], [37, 462, 1.0], [37, 463, 1.0], [37, 464, 1.0], [37, 465, 1.0], [37, 466, 1.0], [37, 467, 1.0], [38, 455, 1.0], [38, 456, 1.0], [38, 457, 1.0], [38, 458, 1.0], [38, 459, 1.0], [38, 460, 1.0], [38, 461, 1.0], [38, 462, 1.0], [38, 463, 1.0], [38, 464, 1.0], [38, 465, 1.0], [38, 466, 1.0], [38, 467, 1.0], [39, 455, 1.0], [39, 456, 1.0], [39, 457, 1.0], [39, 458, 1.0], [39, 459, 1.0], [39, 460, 1.0], [39, 461, 1.0], [39, 462, 1.0], [39, 463, 1.0], [39, 464, 1.0], [39, 465, 1.0], [39, 466, 1.0], [39, 467, 1.0], [40, 455, 1.0], [40, 456, 1.0], [40, 457, 1.0], [40, 458, 1.0], [40, 459, 1.0], [40, 460, 1.0], [40, 461, 1.0], [40, 462, 1.0], [40, 463, 1.0], [40, 464, 1.0], [40, 465, 1.0], [40, 466, 1.0], [41, 455, 1.0], [41, 456, 1.0], [41, 457, 1.0], [41, 458, 1.0], [41, 459, 1.0], [41, 460, 1.0], [41, 461, 1.0], [41, 462, 1.0], [41, 463, 1.0], [41, 464, 1.0], [41, 465, 1.0], [41, 466, 1.0], [42, 455, 1.0], [42, 456, 1.0], [42, 457, 1.0], [42, 458, 1.0], ...] |
| 517473255 | 517473255 | [[41, 493, 1.0], [41, 494, 1.0], [41, 495, 1.0], [41, 496, 1.0], [42, 492, 1.0], [42, 493, 1.0], [42, 494, 1.0], [42, 495, 1.0], [42, 496, 1.0], [42, 497, 1.0], [43, 489, 1.0], [43, 490, 1.0], [43, 491, 1.0], [43, 492, 1.0], [43, 493, 1.0], [43, 494, 1.0], [43, 495, 1.0], [43, 496, 1.0], [43, 497, 1.0], [43, 498, 1.0], [44, 488, 1.0], [44, 489, 1.0], [44, 490, 1.0], [44, 491, 1.0], [44, 492, 1.0], [44, 493, 1.0], [44, 494, 1.0], [44, 495, 1.0], [44, 496, 1.0], [44, 497, 1.0], [44, 498, 1.0], [45, 487, 1.0], [45, 488, 1.0], [45, 489, 1.0], [45, 490, 1.0], [45, 491, 1.0], [45, 492, 1.0], [45, 493, 1.0], [45, 494, 1.0], [45, 495, 1.0], [45, 496, 1.0], [45, 497, 1.0], [45, 498, 1.0], [46, 486, 1.0], [46, 487, 1.0], [46, 488, 1.0], [46, 489, 1.0], [46, 490, 1.0], [46, 491, 1.0], [46, 492, 1.0], [46, 493, 1.0], [46, 494, 1.0], [46, 495, 1.0], [46, 496, 1.0], [46, 497, 1.0], [46, 498, 1.0], [47, 486, 1.0], [47, 487, 1.0], [47, 488, 1.0], [47, 489, 1.0], [47, 490, 1.0], [47, 491, 1.0], [47, 492, 1.0], [47, 493, 1.0], [47, 494, 1.0], [47, 495, 1.0], [47, 496, 1.0], [47, 497, 1.0], [47, 498, 1.0], [47, 499, 1.0], [48, 486, 1.0], [48, 487, 1.0], [48, 488, 1.0], [48, 489, 1.0], [48, 490, 1.0], [48, 491, 1.0], [48, 492, 1.0], [48, 493, 1.0], [48, 494, 1.0], [48, 495, 1.0], [48, 496, 1.0], [48, 497, 1.0], [48, 498, 1.0], [48, 499, 1.0], [49, 486, 1.0], [49, 487, 1.0], [49, 488, 1.0], [49, 489, 1.0], [49, 490, 1.0], [49, 491, 1.0], [49, 492, 1.0], [49, 493, 1.0], [49, 494, 1.0], [49, 495, 1.0], [49, 496, 1.0], [49, 497, 1.0], [49, 498, 1.0], [49, 499, 1.0], [50, 486, 1.0], [50, 487, 1.0], ...] |
... and 170 more rows.
Neuropil
data
timestamps (link to processing/ophys/Fluorescence/Corrected/timestamps)
rois
table
imaging_plane
optical_channel
0
device
grid_spacing
table
| global_roi_id | pixel_mask | |
|---|---|---|
| id | ||
| 517473350 | 517473350 | [[55, 291, 1.0], [55, 292, 1.0], [56, 290, 1.0], [56, 291, 1.0], [56, 292, 1.0], [56, 293, 1.0], [56, 294, 1.0], [56, 295, 1.0], [56, 296, 1.0], [57, 289, 1.0], [57, 290, 1.0], [57, 291, 1.0], [57, 292, 1.0], [57, 293, 1.0], [57, 294, 1.0], [57, 295, 1.0], [57, 296, 1.0], [57, 297, 1.0], [57, 298, 1.0], [58, 288, 1.0], [58, 289, 1.0], [58, 290, 1.0], [58, 291, 1.0], [58, 292, 1.0], [58, 293, 1.0], [58, 294, 1.0], [58, 295, 1.0], [58, 296, 1.0], [58, 297, 1.0], [58, 298, 1.0], [58, 299, 1.0], [59, 288, 1.0], [59, 289, 1.0], [59, 290, 1.0], [59, 291, 1.0], [59, 292, 1.0], [59, 293, 1.0], [59, 294, 1.0], [59, 295, 1.0], [59, 296, 1.0], [59, 297, 1.0], [59, 298, 1.0], [59, 299, 1.0], [60, 288, 1.0], [60, 289, 1.0], [60, 290, 1.0], [60, 291, 1.0], [60, 292, 1.0], [60, 293, 1.0], [60, 294, 1.0], [60, 295, 1.0], [60, 296, 1.0], [60, 297, 1.0], [60, 298, 1.0], [60, 299, 1.0], [61, 287, 1.0], [61, 288, 1.0], [61, 289, 1.0], [61, 290, 1.0], [61, 291, 1.0], [61, 292, 1.0], [61, 293, 1.0], [61, 294, 1.0], [61, 295, 1.0], [61, 296, 1.0], [61, 297, 1.0], [61, 298, 1.0], [61, 299, 1.0], [62, 287, 1.0], [62, 288, 1.0], [62, 289, 1.0], [62, 290, 1.0], [62, 291, 1.0], [62, 292, 1.0], [62, 293, 1.0], [62, 294, 1.0], [62, 295, 1.0], [62, 296, 1.0], [62, 297, 1.0], [62, 298, 1.0], [62, 299, 1.0], [63, 287, 1.0], [63, 288, 1.0], [63, 289, 1.0], [63, 290, 1.0], [63, 291, 1.0], [63, 292, 1.0], [63, 293, 1.0], [63, 294, 1.0], [63, 295, 1.0], [63, 296, 1.0], [63, 297, 1.0], [63, 298, 1.0], [63, 299, 1.0], [64, 287, 1.0], [64, 288, 1.0], [64, 289, 1.0], [64, 290, 1.0], [64, 291, 1.0], [64, 292, 1.0], ...] |
| 517473341 | 517473341 | [[58, 272, 1.0], [58, 273, 1.0], [58, 274, 1.0], [58, 275, 1.0], [58, 276, 1.0], [58, 277, 1.0], [59, 271, 1.0], [59, 272, 1.0], [59, 273, 1.0], [59, 274, 1.0], [59, 275, 1.0], [59, 276, 1.0], [59, 277, 1.0], [59, 278, 1.0], [59, 279, 1.0], [59, 280, 1.0], [59, 281, 1.0], [59, 282, 1.0], [60, 271, 1.0], [60, 272, 1.0], [60, 273, 1.0], [60, 274, 1.0], [60, 275, 1.0], [60, 276, 1.0], [60, 277, 1.0], [60, 278, 1.0], [60, 279, 1.0], [60, 280, 1.0], [60, 281, 1.0], [60, 282, 1.0], [60, 283, 1.0], [61, 270, 1.0], [61, 271, 1.0], [61, 272, 1.0], [61, 273, 1.0], [61, 274, 1.0], [61, 275, 1.0], [61, 276, 1.0], [61, 277, 1.0], [61, 278, 1.0], [61, 279, 1.0], [61, 280, 1.0], [61, 281, 1.0], [61, 282, 1.0], [62, 270, 1.0], [62, 271, 1.0], [62, 272, 1.0], [62, 273, 1.0], [62, 274, 1.0], [62, 275, 1.0], [62, 276, 1.0], [62, 277, 1.0], [62, 278, 1.0], [62, 279, 1.0], [62, 280, 1.0], [62, 281, 1.0], [62, 282, 1.0], [63, 269, 1.0], [63, 270, 1.0], [63, 271, 1.0], [63, 272, 1.0], [63, 273, 1.0], [63, 274, 1.0], [63, 275, 1.0], [63, 276, 1.0], [63, 277, 1.0], [63, 278, 1.0], [63, 279, 1.0], [63, 280, 1.0], [63, 281, 1.0], [63, 282, 1.0], [64, 268, 1.0], [64, 269, 1.0], [64, 270, 1.0], [64, 271, 1.0], [64, 272, 1.0], [64, 273, 1.0], [64, 274, 1.0], [64, 275, 1.0], [64, 276, 1.0], [64, 277, 1.0], [64, 278, 1.0], [64, 279, 1.0], [64, 280, 1.0], [64, 281, 1.0], [64, 282, 1.0], [65, 268, 1.0], [65, 269, 1.0], [65, 270, 1.0], [65, 271, 1.0], [65, 272, 1.0], [65, 273, 1.0], [65, 274, 1.0], [65, 275, 1.0], [65, 276, 1.0], [65, 277, 1.0], [65, 278, 1.0], [65, 279, 1.0], [65, 280, 1.0], [65, 281, 1.0], ...] |
| 517473313 | 517473313 | [[33, 459, 1.0], [33, 460, 1.0], [33, 461, 1.0], [33, 462, 1.0], [33, 463, 1.0], [34, 458, 1.0], [34, 459, 1.0], [34, 460, 1.0], [34, 461, 1.0], [34, 462, 1.0], [34, 463, 1.0], [34, 464, 1.0], [35, 457, 1.0], [35, 458, 1.0], [35, 459, 1.0], [35, 460, 1.0], [35, 461, 1.0], [35, 462, 1.0], [35, 463, 1.0], [35, 464, 1.0], [35, 465, 1.0], [35, 466, 1.0], [36, 456, 1.0], [36, 457, 1.0], [36, 458, 1.0], [36, 459, 1.0], [36, 460, 1.0], [36, 461, 1.0], [36, 462, 1.0], [36, 463, 1.0], [36, 464, 1.0], [36, 465, 1.0], [36, 466, 1.0], [36, 467, 1.0], [37, 456, 1.0], [37, 457, 1.0], [37, 458, 1.0], [37, 459, 1.0], [37, 460, 1.0], [37, 461, 1.0], [37, 462, 1.0], [37, 463, 1.0], [37, 464, 1.0], [37, 465, 1.0], [37, 466, 1.0], [37, 467, 1.0], [38, 455, 1.0], [38, 456, 1.0], [38, 457, 1.0], [38, 458, 1.0], [38, 459, 1.0], [38, 460, 1.0], [38, 461, 1.0], [38, 462, 1.0], [38, 463, 1.0], [38, 464, 1.0], [38, 465, 1.0], [38, 466, 1.0], [38, 467, 1.0], [39, 455, 1.0], [39, 456, 1.0], [39, 457, 1.0], [39, 458, 1.0], [39, 459, 1.0], [39, 460, 1.0], [39, 461, 1.0], [39, 462, 1.0], [39, 463, 1.0], [39, 464, 1.0], [39, 465, 1.0], [39, 466, 1.0], [39, 467, 1.0], [40, 455, 1.0], [40, 456, 1.0], [40, 457, 1.0], [40, 458, 1.0], [40, 459, 1.0], [40, 460, 1.0], [40, 461, 1.0], [40, 462, 1.0], [40, 463, 1.0], [40, 464, 1.0], [40, 465, 1.0], [40, 466, 1.0], [41, 455, 1.0], [41, 456, 1.0], [41, 457, 1.0], [41, 458, 1.0], [41, 459, 1.0], [41, 460, 1.0], [41, 461, 1.0], [41, 462, 1.0], [41, 463, 1.0], [41, 464, 1.0], [41, 465, 1.0], [41, 466, 1.0], [42, 455, 1.0], [42, 456, 1.0], [42, 457, 1.0], [42, 458, 1.0], ...] |
| 517473255 | 517473255 | [[41, 493, 1.0], [41, 494, 1.0], [41, 495, 1.0], [41, 496, 1.0], [42, 492, 1.0], [42, 493, 1.0], [42, 494, 1.0], [42, 495, 1.0], [42, 496, 1.0], [42, 497, 1.0], [43, 489, 1.0], [43, 490, 1.0], [43, 491, 1.0], [43, 492, 1.0], [43, 493, 1.0], [43, 494, 1.0], [43, 495, 1.0], [43, 496, 1.0], [43, 497, 1.0], [43, 498, 1.0], [44, 488, 1.0], [44, 489, 1.0], [44, 490, 1.0], [44, 491, 1.0], [44, 492, 1.0], [44, 493, 1.0], [44, 494, 1.0], [44, 495, 1.0], [44, 496, 1.0], [44, 497, 1.0], [44, 498, 1.0], [45, 487, 1.0], [45, 488, 1.0], [45, 489, 1.0], [45, 490, 1.0], [45, 491, 1.0], [45, 492, 1.0], [45, 493, 1.0], [45, 494, 1.0], [45, 495, 1.0], [45, 496, 1.0], [45, 497, 1.0], [45, 498, 1.0], [46, 486, 1.0], [46, 487, 1.0], [46, 488, 1.0], [46, 489, 1.0], [46, 490, 1.0], [46, 491, 1.0], [46, 492, 1.0], [46, 493, 1.0], [46, 494, 1.0], [46, 495, 1.0], [46, 496, 1.0], [46, 497, 1.0], [46, 498, 1.0], [47, 486, 1.0], [47, 487, 1.0], [47, 488, 1.0], [47, 489, 1.0], [47, 490, 1.0], [47, 491, 1.0], [47, 492, 1.0], [47, 493, 1.0], [47, 494, 1.0], [47, 495, 1.0], [47, 496, 1.0], [47, 497, 1.0], [47, 498, 1.0], [47, 499, 1.0], [48, 486, 1.0], [48, 487, 1.0], [48, 488, 1.0], [48, 489, 1.0], [48, 490, 1.0], [48, 491, 1.0], [48, 492, 1.0], [48, 493, 1.0], [48, 494, 1.0], [48, 495, 1.0], [48, 496, 1.0], [48, 497, 1.0], [48, 498, 1.0], [48, 499, 1.0], [49, 486, 1.0], [49, 487, 1.0], [49, 488, 1.0], [49, 489, 1.0], [49, 490, 1.0], [49, 491, 1.0], [49, 492, 1.0], [49, 493, 1.0], [49, 494, 1.0], [49, 495, 1.0], [49, 496, 1.0], [49, 497, 1.0], [49, 498, 1.0], [49, 499, 1.0], [50, 486, 1.0], [50, 487, 1.0], ...] |
... and 170 more rows.
ImageSegmentation
plane_segmentations
PlaneSegmentation
imaging_plane
optical_channel
0
device
grid_spacing
table
| global_roi_id | pixel_mask | |
|---|---|---|
| id | ||
| 517473350 | 517473350 | [[55, 291, 1.0], [55, 292, 1.0], [56, 290, 1.0], [56, 291, 1.0], [56, 292, 1.0], [56, 293, 1.0], [56, 294, 1.0], [56, 295, 1.0], [56, 296, 1.0], [57, 289, 1.0], [57, 290, 1.0], [57, 291, 1.0], [57, 292, 1.0], [57, 293, 1.0], [57, 294, 1.0], [57, 295, 1.0], [57, 296, 1.0], [57, 297, 1.0], [57, 298, 1.0], [58, 288, 1.0], [58, 289, 1.0], [58, 290, 1.0], [58, 291, 1.0], [58, 292, 1.0], [58, 293, 1.0], [58, 294, 1.0], [58, 295, 1.0], [58, 296, 1.0], [58, 297, 1.0], [58, 298, 1.0], [58, 299, 1.0], [59, 288, 1.0], [59, 289, 1.0], [59, 290, 1.0], [59, 291, 1.0], [59, 292, 1.0], [59, 293, 1.0], [59, 294, 1.0], [59, 295, 1.0], [59, 296, 1.0], [59, 297, 1.0], [59, 298, 1.0], [59, 299, 1.0], [60, 288, 1.0], [60, 289, 1.0], [60, 290, 1.0], [60, 291, 1.0], [60, 292, 1.0], [60, 293, 1.0], [60, 294, 1.0], [60, 295, 1.0], [60, 296, 1.0], [60, 297, 1.0], [60, 298, 1.0], [60, 299, 1.0], [61, 287, 1.0], [61, 288, 1.0], [61, 289, 1.0], [61, 290, 1.0], [61, 291, 1.0], [61, 292, 1.0], [61, 293, 1.0], [61, 294, 1.0], [61, 295, 1.0], [61, 296, 1.0], [61, 297, 1.0], [61, 298, 1.0], [61, 299, 1.0], [62, 287, 1.0], [62, 288, 1.0], [62, 289, 1.0], [62, 290, 1.0], [62, 291, 1.0], [62, 292, 1.0], [62, 293, 1.0], [62, 294, 1.0], [62, 295, 1.0], [62, 296, 1.0], [62, 297, 1.0], [62, 298, 1.0], [62, 299, 1.0], [63, 287, 1.0], [63, 288, 1.0], [63, 289, 1.0], [63, 290, 1.0], [63, 291, 1.0], [63, 292, 1.0], [63, 293, 1.0], [63, 294, 1.0], [63, 295, 1.0], [63, 296, 1.0], [63, 297, 1.0], [63, 298, 1.0], [63, 299, 1.0], [64, 287, 1.0], [64, 288, 1.0], [64, 289, 1.0], [64, 290, 1.0], [64, 291, 1.0], [64, 292, 1.0], ...] |
| 517473341 | 517473341 | [[58, 272, 1.0], [58, 273, 1.0], [58, 274, 1.0], [58, 275, 1.0], [58, 276, 1.0], [58, 277, 1.0], [59, 271, 1.0], [59, 272, 1.0], [59, 273, 1.0], [59, 274, 1.0], [59, 275, 1.0], [59, 276, 1.0], [59, 277, 1.0], [59, 278, 1.0], [59, 279, 1.0], [59, 280, 1.0], [59, 281, 1.0], [59, 282, 1.0], [60, 271, 1.0], [60, 272, 1.0], [60, 273, 1.0], [60, 274, 1.0], [60, 275, 1.0], [60, 276, 1.0], [60, 277, 1.0], [60, 278, 1.0], [60, 279, 1.0], [60, 280, 1.0], [60, 281, 1.0], [60, 282, 1.0], [60, 283, 1.0], [61, 270, 1.0], [61, 271, 1.0], [61, 272, 1.0], [61, 273, 1.0], [61, 274, 1.0], [61, 275, 1.0], [61, 276, 1.0], [61, 277, 1.0], [61, 278, 1.0], [61, 279, 1.0], [61, 280, 1.0], [61, 281, 1.0], [61, 282, 1.0], [62, 270, 1.0], [62, 271, 1.0], [62, 272, 1.0], [62, 273, 1.0], [62, 274, 1.0], [62, 275, 1.0], [62, 276, 1.0], [62, 277, 1.0], [62, 278, 1.0], [62, 279, 1.0], [62, 280, 1.0], [62, 281, 1.0], [62, 282, 1.0], [63, 269, 1.0], [63, 270, 1.0], [63, 271, 1.0], [63, 272, 1.0], [63, 273, 1.0], [63, 274, 1.0], [63, 275, 1.0], [63, 276, 1.0], [63, 277, 1.0], [63, 278, 1.0], [63, 279, 1.0], [63, 280, 1.0], [63, 281, 1.0], [63, 282, 1.0], [64, 268, 1.0], [64, 269, 1.0], [64, 270, 1.0], [64, 271, 1.0], [64, 272, 1.0], [64, 273, 1.0], [64, 274, 1.0], [64, 275, 1.0], [64, 276, 1.0], [64, 277, 1.0], [64, 278, 1.0], [64, 279, 1.0], [64, 280, 1.0], [64, 281, 1.0], [64, 282, 1.0], [65, 268, 1.0], [65, 269, 1.0], [65, 270, 1.0], [65, 271, 1.0], [65, 272, 1.0], [65, 273, 1.0], [65, 274, 1.0], [65, 275, 1.0], [65, 276, 1.0], [65, 277, 1.0], [65, 278, 1.0], [65, 279, 1.0], [65, 280, 1.0], [65, 281, 1.0], ...] |
| 517473313 | 517473313 | [[33, 459, 1.0], [33, 460, 1.0], [33, 461, 1.0], [33, 462, 1.0], [33, 463, 1.0], [34, 458, 1.0], [34, 459, 1.0], [34, 460, 1.0], [34, 461, 1.0], [34, 462, 1.0], [34, 463, 1.0], [34, 464, 1.0], [35, 457, 1.0], [35, 458, 1.0], [35, 459, 1.0], [35, 460, 1.0], [35, 461, 1.0], [35, 462, 1.0], [35, 463, 1.0], [35, 464, 1.0], [35, 465, 1.0], [35, 466, 1.0], [36, 456, 1.0], [36, 457, 1.0], [36, 458, 1.0], [36, 459, 1.0], [36, 460, 1.0], [36, 461, 1.0], [36, 462, 1.0], [36, 463, 1.0], [36, 464, 1.0], [36, 465, 1.0], [36, 466, 1.0], [36, 467, 1.0], [37, 456, 1.0], [37, 457, 1.0], [37, 458, 1.0], [37, 459, 1.0], [37, 460, 1.0], [37, 461, 1.0], [37, 462, 1.0], [37, 463, 1.0], [37, 464, 1.0], [37, 465, 1.0], [37, 466, 1.0], [37, 467, 1.0], [38, 455, 1.0], [38, 456, 1.0], [38, 457, 1.0], [38, 458, 1.0], [38, 459, 1.0], [38, 460, 1.0], [38, 461, 1.0], [38, 462, 1.0], [38, 463, 1.0], [38, 464, 1.0], [38, 465, 1.0], [38, 466, 1.0], [38, 467, 1.0], [39, 455, 1.0], [39, 456, 1.0], [39, 457, 1.0], [39, 458, 1.0], [39, 459, 1.0], [39, 460, 1.0], [39, 461, 1.0], [39, 462, 1.0], [39, 463, 1.0], [39, 464, 1.0], [39, 465, 1.0], [39, 466, 1.0], [39, 467, 1.0], [40, 455, 1.0], [40, 456, 1.0], [40, 457, 1.0], [40, 458, 1.0], [40, 459, 1.0], [40, 460, 1.0], [40, 461, 1.0], [40, 462, 1.0], [40, 463, 1.0], [40, 464, 1.0], [40, 465, 1.0], [40, 466, 1.0], [41, 455, 1.0], [41, 456, 1.0], [41, 457, 1.0], [41, 458, 1.0], [41, 459, 1.0], [41, 460, 1.0], [41, 461, 1.0], [41, 462, 1.0], [41, 463, 1.0], [41, 464, 1.0], [41, 465, 1.0], [41, 466, 1.0], [42, 455, 1.0], [42, 456, 1.0], [42, 457, 1.0], [42, 458, 1.0], ...] |
| 517473255 | 517473255 | [[41, 493, 1.0], [41, 494, 1.0], [41, 495, 1.0], [41, 496, 1.0], [42, 492, 1.0], [42, 493, 1.0], [42, 494, 1.0], [42, 495, 1.0], [42, 496, 1.0], [42, 497, 1.0], [43, 489, 1.0], [43, 490, 1.0], [43, 491, 1.0], [43, 492, 1.0], [43, 493, 1.0], [43, 494, 1.0], [43, 495, 1.0], [43, 496, 1.0], [43, 497, 1.0], [43, 498, 1.0], [44, 488, 1.0], [44, 489, 1.0], [44, 490, 1.0], [44, 491, 1.0], [44, 492, 1.0], [44, 493, 1.0], [44, 494, 1.0], [44, 495, 1.0], [44, 496, 1.0], [44, 497, 1.0], [44, 498, 1.0], [45, 487, 1.0], [45, 488, 1.0], [45, 489, 1.0], [45, 490, 1.0], [45, 491, 1.0], [45, 492, 1.0], [45, 493, 1.0], [45, 494, 1.0], [45, 495, 1.0], [45, 496, 1.0], [45, 497, 1.0], [45, 498, 1.0], [46, 486, 1.0], [46, 487, 1.0], [46, 488, 1.0], [46, 489, 1.0], [46, 490, 1.0], [46, 491, 1.0], [46, 492, 1.0], [46, 493, 1.0], [46, 494, 1.0], [46, 495, 1.0], [46, 496, 1.0], [46, 497, 1.0], [46, 498, 1.0], [47, 486, 1.0], [47, 487, 1.0], [47, 488, 1.0], [47, 489, 1.0], [47, 490, 1.0], [47, 491, 1.0], [47, 492, 1.0], [47, 493, 1.0], [47, 494, 1.0], [47, 495, 1.0], [47, 496, 1.0], [47, 497, 1.0], [47, 498, 1.0], [47, 499, 1.0], [48, 486, 1.0], [48, 487, 1.0], [48, 488, 1.0], [48, 489, 1.0], [48, 490, 1.0], [48, 491, 1.0], [48, 492, 1.0], [48, 493, 1.0], [48, 494, 1.0], [48, 495, 1.0], [48, 496, 1.0], [48, 497, 1.0], [48, 498, 1.0], [48, 499, 1.0], [49, 486, 1.0], [49, 487, 1.0], [49, 488, 1.0], [49, 489, 1.0], [49, 490, 1.0], [49, 491, 1.0], [49, 492, 1.0], [49, 493, 1.0], [49, 494, 1.0], [49, 495, 1.0], [49, 496, 1.0], [49, 497, 1.0], [49, 498, 1.0], [49, 499, 1.0], [50, 486, 1.0], [50, 487, 1.0], ...] |
... and 170 more rows.
SummaryImages
images
maximum_intensity_projection
ContaminationRatios
table
| ratios | ratios_rmse | |
|---|---|---|
| id | ||
| 0 | 0.078 | 0.003927 |
| 1 | 0.125 | 0.003921 |
| 2 | 0.188 | 0.003701 |
| 3 | 0.165 | 0.003572 |
... and 170 more rows.
epoch_tags
set()devices
Camera
Microscope
StimulusDisplay
imaging_planes
ImagingPlane
optical_channel
0
device
grid_spacing
intervals
epochs
table
| start_time | stop_time | stimulus_type | |
|---|---|---|---|
| id | |||
| 0 | 51.55381 | 531.93378 | static_gratings |
| 1 | 561.98893 | 1042.44259 | natural_scenes |
| 2 | 1047.42969 | 1342.66703 | spontaneous |
| 3 | 1342.70028 | 1823.72944 | natural_scenes |
... and 4 more rows.
subject
epochs
table
| start_time | stop_time | stimulus_type | |
|---|---|---|---|
| id | |||
| 0 | 51.55381 | 531.93378 | static_gratings |
| 1 | 561.98893 | 1042.44259 | natural_scenes |
| 2 | 1047.42969 | 1342.66703 | spontaneous |
| 3 | 1342.70028 | 1823.72944 | natural_scenes |
... and 4 more rows.
Part 3: Loading specific fields of the data for a single session
Begin by defining some plotting functions to simplify cells throughout the later sections...
def plot_image(
*, # "*" character forces keyword argument usage, which makes code much more readable
image: numpy.ndarray,
size: int = 8,
color_map: Union[str, None] = None
) -> None:
"""
Pretty rendering of a simple square grayscale image.
Axis labels and ticks are stripped since their size is viewable from the image array shape.
Also, display the color bar at a good size if color is not gray.
"""
matplotlib_pyplot.figure(figsize=(size, size))
frame = matplotlib_pyplot.gca()
for label in frame.axes.get_xticklabels():
label.set_visible(False)
label.set_fontsize(0.0)
for label in frame.axes.get_yticklabels():
label.set_fontsize(0.0)
label.set_visible(False)
for tick in frame.axes.get_xticklines():
tick.set_visible(False)
for tick in frame.axes.get_yticklines():
tick.set_visible(False)
plot = matplotlib_pyplot.imshow(image, cmap=color_map);
if color_map != "gray":
color_bar = matplotlib_pyplot.colorbar(plot, fraction=0.046, pad=0.04)
color_bar.solids.set_edgecolor("face")
def plot_traces(
*,
timestamps: numpy.ndarray,
traces: numpy.ndarray,
hide_y_scale: bool = True,
x_axis_label: Union[str, None] = "Time (s)", # Since timestamps in seconds are so common in NWB, just make it the default
y_axis_label: Union[str, None] = None,
padding: float = 2.0,
trace_color: Union[str, None] = None,
reset_figure: bool = True,
figure_shape: Tuple[int, int] = (10, 4),
return_frame: bool = False,
) -> Union[matplotlib.axes.Axes, None]:
"""
Pretty rendering of traces of activity (such as ROI or running speed) over time.
The Y axis of ROI traces is rarely known in practice, so numerical scale doesn't really matter for plotting.
Also, timestamps from NWB will always be in seconds.
"""
if reset_figure:
matplotlib_pyplot.figure(figsize=figure_shape)
if len(traces.shape) == 1:
matplotlib_pyplot.plot(timestamps, traces, color=trace_color)
else:
for trace_index in range(traces.shape[1]):
matplotlib_pyplot.plot(
timestamps,
traces[:, trace_index] + (trace_index * padding), # Add vertical padding to separate each trace
color=trace_color,
)
frame = matplotlib_pyplot.gca()
frame.axes.spines['top'].set_visible(False)
frame.axes.spines['right'].set_visible(False)
frame.axes.spines['bottom'].set_visible(False)
frame.axes.spines['left'].set_visible(False)
if hide_y_scale:
for label in frame.axes.get_yticklabels():
label.set_fontsize(0.0)
label.set_visible(False)
for tick in frame.axes.get_yticklines():
tick.set_visible(False)
matplotlib_pyplot.xlabel(x_axis_label, fontsize=16)
if y_axis_label is not None:
matplotlib_pyplot.ylabel(y_axis_label, fontsize=16)
if return_frame:
return frame
def apply_epoch_shading(
*,
frame: matplotlib.axes.Axes,
epoch_table: pynwb.file.TimeIntervals,
) -> None:
"""For each epoch, shade the plot based on the stimulus type."""
# Clear previous legend to prevent appending
frame.legend([])
epoch_table_data_frame = epoch_table.to_dataframe()
stimulus_type_to_color = dict(
spontaneous="grey",
static_gratings="fuchsia",
drifting_gratings="lavender",
natural_scenes="orange",
natural_movie_one="green",
natural_movie_two="red",
natural_movie_three="blue",
)
stimulus_type_to_label = dict(
spontaneous="Spontaneous",
static_gratings="Static Gratings",
drifting_gratings="Drifting Gratings",
natural_scenes="Natural Scenes",
natural_movie_one="Natural Movie (1)",
natural_movie_two="Natural Movie (2)",
natural_movie_three="Natural Movie (3)",
)
patches= list()
for stimulus_name in epoch_table_data_frame.stimulus_type.unique():
stimulus = epoch_table_data_frame[epoch_table_data_frame.stimulus_type==stimulus_name]
if len(stimulus) > 0:
patches.append(
matplotlib.patches.Patch(
color=stimulus_type_to_color[stimulus_name],
label=stimulus_type_to_label[stimulus_name],
alpha=0.1,
)
)
for _, row in stimulus.iterrows():
frame.axvspan(
xmin=row.start_time, xmax=row.stop_time, color=stimulus_type_to_color[stimulus_name], alpha=0.1
)
frame.legend(title="Epochs", loc="center left", handles=patches, shadow=True, fancybox=True, bbox_to_anchor=(1, 0.5));
3.0 Raw Motion Corrected Movies¶
Most of the sessions in this dataset have the full original motion corrected microscopy data available - that would be around 80 TB if we uncompressed it!
Thankfully, we never need to download the data (unless we want to work offline).
However, since people do like to occasionally download local copies of the processed data, the raw data is stored in separate files that are accessed less frequently.
nwbfile_with_raw_imaging = read_visual_coding_ophys_file(dandiset=dandiset, session_id=session_id, raw_or_processed="raw")
nwbfile_with_raw_imaging
root (NWBFile)
session_start_time
2016-02-04 10:25:24-08:00timestamps_reference_time
2016-02-04 10:25:24-08:00file_create_date
0
2024-03-23 01:03:19.871158-04:00acquisition
MotionCorrectedTwoPhotonSeries
data
timestamps
imaging_plane
optical_channel
0
device
grid_spacing
timestamp_link
MotionCorrectionShiftsPerFrame
data
timestamps (link to acquisition/MotionCorrectedTwoPhotonSeries/timestamps)
epoch_tags
set()devices
Microscope
imaging_planes
ImagingPlane
optical_channel
0
device
grid_spacing
subject
motion_corrected_imaging = nwbfile_with_raw_imaging.acquisition["MotionCorrectedTwoPhotonSeries"]
motion_corrected_imaging
MotionCorrectedTwoPhotonSeries (TwoPhotonSeries)
data
timestamps
imaging_plane
optical_channel
0
device
grid_spacing
timestamp_link
plot_image(image=motion_corrected_imaging.data[3_000, ...], color_map="gray")
That looks pretty noisy! The following sections will examine what the data looks like after it's been thoroughly cleaned.
3.1 Maximum projection¶
This is the projection of the full motion corrected movie. It shows all of the cells imaged during the session.
maximum_intensity_projection = nwbfile.processing["ophys"]["SummaryImages"]["maximum_intensity_projection"][:]
maximum_intensity_projection
array([[ 296, 308, 306, ..., 2760, 2164, 888],
[ 312, 298, 303, ..., 2741, 1716, 1031],
[ 278, 295, 298, ..., 1426, 929, 574],
...,
[ 347, 348, 366, ..., 407, 403, 410],
[ 335, 357, 398, ..., 317, 354, 362],
[ 308, 288, 306, ..., 293, 316, 318]], dtype=uint16)
maximum_intensity_projection.shape
(512, 512)
[:] is what triggers the data to be sent from the archive. You can even limit the amount of data requested by choosing a subrange!
plot_image(image=maximum_intensity_projection, color_map="gray")
3.2 PlaneSegmentation¶
These are all of the segmented masks for cell bodies in this experiment. We can view a table summary of these by calling to_dataframe().
plane_segmentation = nwbfile.processing["ophys"]["ImageSegmentation"]["PlaneSegmentation"]
plane_segmentation_table = plane_segmentation.to_dataframe()
plane_segmentation_table
| global_roi_id | pixel_mask | |
|---|---|---|
| id | ||
| 517473350 | 517473350 | [[55, 291, 1.0], [55, 292, 1.0], [56, 290, 1.0... |
| 517473341 | 517473341 | [[58, 272, 1.0], [58, 273, 1.0], [58, 274, 1.0... |
| 517473313 | 517473313 | [[33, 459, 1.0], [33, 460, 1.0], [33, 461, 1.0... |
| 517473255 | 517473255 | [[41, 493, 1.0], [41, 494, 1.0], [41, 495, 1.0... |
| 517471959 | 517471959 | [[19, 346, 1.0], [19, 347, 1.0], [19, 348, 1.0... |
| ... | ... | ... |
| 517473240 | 517473240 | [[127, 125, 1.0], [127, 126, 1.0], [127, 127, ... |
| 587376723 | 587376723 | [[132, 187, 1.0], [132, 188, 1.0], [132, 189, ... |
| 517472450 | 517472450 | [[129, 87, 1.0], [130, 87, 1.0], [130, 88, 1.0... |
| 517473191 | 517473191 | [[195, 264, 1.0], [195, 265, 1.0], [195, 266, ... |
| 517471925 | 517471925 | [[157, 160, 1.0], [158, 160, 1.0], [158, 161, ... |
174 rows × 2 columns
Notice that each cell in the dataset has two IDs: a local ID specific to this table and session, and a unique global ID.
The global ID can be used to find experiments/session for a specified cell. It is also valuable for finding the matched cell across sessions for the same experiment container (e.g., if you want to compare a cell's response to drifting gratings with its response the natural scenes).
Pixel masks are a data structure that record a list of pixels (width x height) and weights that outline each ROI.
pixel_masks = plane_segmentation["pixel_mask"][:]
pixel_masks[0][:10]
array([(55, 291, 1.), (55, 292, 1.), (56, 290, 1.), (56, 291, 1.),
(56, 292, 1.), (56, 293, 1.), (56, 294, 1.), (56, 295, 1.),
(56, 296, 1.), (57, 289, 1.)],
dtype=[('x', '<u4'), ('y', '<u4'), ('weight', '<f4')])
combined_image_masks = numpy.zeros(shape=maximum_intensity_projection.shape)
for pixel_mask in plane_segmentation["pixel_mask"][:]:
for x, y, w in pixel_mask:
combined_image_masks[x,y] += w
Brighter values indicate overlap across multiple ROIs (since all of our weights are
1.0).
plot_image(image=combined_image_masks)
3.3 Segmented and Processed Traces¶
There are a number of accessible traces in the NWB file, including raw fluorescence, neuropil corrected traces, demixed traces, and $\Delta F / F$ traces.
These are split across two containers; the DfOverF (containing only a single calculated trace) and Fluorescence from which it was calculated (containing three individual steps of the processing pipeline).
Be sure to read the description of each object to understand how they relate to each other! The following image from the Allen Institute does a good job explaining the workflow.
nwbfile.processing["ophys"]["DfOverF"]
DfOverF
roi_response_series
DfOverF
data
timestamps (link to processing/ophys/Fluorescence/Corrected/timestamps)
rois
table
imaging_plane
optical_channel
0
device
grid_spacing
table
| global_roi_id | pixel_mask | |
|---|---|---|
| id | ||
| 517473350 | 517473350 | [[55, 291, 1.0], [55, 292, 1.0], [56, 290, 1.0], [56, 291, 1.0], [56, 292, 1.0], [56, 293, 1.0], [56, 294, 1.0], [56, 295, 1.0], [56, 296, 1.0], [57, 289, 1.0], [57, 290, 1.0], [57, 291, 1.0], [57, 292, 1.0], [57, 293, 1.0], [57, 294, 1.0], [57, 295, 1.0], [57, 296, 1.0], [57, 297, 1.0], [57, 298, 1.0], [58, 288, 1.0], [58, 289, 1.0], [58, 290, 1.0], [58, 291, 1.0], [58, 292, 1.0], [58, 293, 1.0], [58, 294, 1.0], [58, 295, 1.0], [58, 296, 1.0], [58, 297, 1.0], [58, 298, 1.0], [58, 299, 1.0], [59, 288, 1.0], [59, 289, 1.0], [59, 290, 1.0], [59, 291, 1.0], [59, 292, 1.0], [59, 293, 1.0], [59, 294, 1.0], [59, 295, 1.0], [59, 296, 1.0], [59, 297, 1.0], [59, 298, 1.0], [59, 299, 1.0], [60, 288, 1.0], [60, 289, 1.0], [60, 290, 1.0], [60, 291, 1.0], [60, 292, 1.0], [60, 293, 1.0], [60, 294, 1.0], [60, 295, 1.0], [60, 296, 1.0], [60, 297, 1.0], [60, 298, 1.0], [60, 299, 1.0], [61, 287, 1.0], [61, 288, 1.0], [61, 289, 1.0], [61, 290, 1.0], [61, 291, 1.0], [61, 292, 1.0], [61, 293, 1.0], [61, 294, 1.0], [61, 295, 1.0], [61, 296, 1.0], [61, 297, 1.0], [61, 298, 1.0], [61, 299, 1.0], [62, 287, 1.0], [62, 288, 1.0], [62, 289, 1.0], [62, 290, 1.0], [62, 291, 1.0], [62, 292, 1.0], [62, 293, 1.0], [62, 294, 1.0], [62, 295, 1.0], [62, 296, 1.0], [62, 297, 1.0], [62, 298, 1.0], [62, 299, 1.0], [63, 287, 1.0], [63, 288, 1.0], [63, 289, 1.0], [63, 290, 1.0], [63, 291, 1.0], [63, 292, 1.0], [63, 293, 1.0], [63, 294, 1.0], [63, 295, 1.0], [63, 296, 1.0], [63, 297, 1.0], [63, 298, 1.0], [63, 299, 1.0], [64, 287, 1.0], [64, 288, 1.0], [64, 289, 1.0], [64, 290, 1.0], [64, 291, 1.0], [64, 292, 1.0], ...] |
| 517473341 | 517473341 | [[58, 272, 1.0], [58, 273, 1.0], [58, 274, 1.0], [58, 275, 1.0], [58, 276, 1.0], [58, 277, 1.0], [59, 271, 1.0], [59, 272, 1.0], [59, 273, 1.0], [59, 274, 1.0], [59, 275, 1.0], [59, 276, 1.0], [59, 277, 1.0], [59, 278, 1.0], [59, 279, 1.0], [59, 280, 1.0], [59, 281, 1.0], [59, 282, 1.0], [60, 271, 1.0], [60, 272, 1.0], [60, 273, 1.0], [60, 274, 1.0], [60, 275, 1.0], [60, 276, 1.0], [60, 277, 1.0], [60, 278, 1.0], [60, 279, 1.0], [60, 280, 1.0], [60, 281, 1.0], [60, 282, 1.0], [60, 283, 1.0], [61, 270, 1.0], [61, 271, 1.0], [61, 272, 1.0], [61, 273, 1.0], [61, 274, 1.0], [61, 275, 1.0], [61, 276, 1.0], [61, 277, 1.0], [61, 278, 1.0], [61, 279, 1.0], [61, 280, 1.0], [61, 281, 1.0], [61, 282, 1.0], [62, 270, 1.0], [62, 271, 1.0], [62, 272, 1.0], [62, 273, 1.0], [62, 274, 1.0], [62, 275, 1.0], [62, 276, 1.0], [62, 277, 1.0], [62, 278, 1.0], [62, 279, 1.0], [62, 280, 1.0], [62, 281, 1.0], [62, 282, 1.0], [63, 269, 1.0], [63, 270, 1.0], [63, 271, 1.0], [63, 272, 1.0], [63, 273, 1.0], [63, 274, 1.0], [63, 275, 1.0], [63, 276, 1.0], [63, 277, 1.0], [63, 278, 1.0], [63, 279, 1.0], [63, 280, 1.0], [63, 281, 1.0], [63, 282, 1.0], [64, 268, 1.0], [64, 269, 1.0], [64, 270, 1.0], [64, 271, 1.0], [64, 272, 1.0], [64, 273, 1.0], [64, 274, 1.0], [64, 275, 1.0], [64, 276, 1.0], [64, 277, 1.0], [64, 278, 1.0], [64, 279, 1.0], [64, 280, 1.0], [64, 281, 1.0], [64, 282, 1.0], [65, 268, 1.0], [65, 269, 1.0], [65, 270, 1.0], [65, 271, 1.0], [65, 272, 1.0], [65, 273, 1.0], [65, 274, 1.0], [65, 275, 1.0], [65, 276, 1.0], [65, 277, 1.0], [65, 278, 1.0], [65, 279, 1.0], [65, 280, 1.0], [65, 281, 1.0], ...] |
| 517473313 | 517473313 | [[33, 459, 1.0], [33, 460, 1.0], [33, 461, 1.0], [33, 462, 1.0], [33, 463, 1.0], [34, 458, 1.0], [34, 459, 1.0], [34, 460, 1.0], [34, 461, 1.0], [34, 462, 1.0], [34, 463, 1.0], [34, 464, 1.0], [35, 457, 1.0], [35, 458, 1.0], [35, 459, 1.0], [35, 460, 1.0], [35, 461, 1.0], [35, 462, 1.0], [35, 463, 1.0], [35, 464, 1.0], [35, 465, 1.0], [35, 466, 1.0], [36, 456, 1.0], [36, 457, 1.0], [36, 458, 1.0], [36, 459, 1.0], [36, 460, 1.0], [36, 461, 1.0], [36, 462, 1.0], [36, 463, 1.0], [36, 464, 1.0], [36, 465, 1.0], [36, 466, 1.0], [36, 467, 1.0], [37, 456, 1.0], [37, 457, 1.0], [37, 458, 1.0], [37, 459, 1.0], [37, 460, 1.0], [37, 461, 1.0], [37, 462, 1.0], [37, 463, 1.0], [37, 464, 1.0], [37, 465, 1.0], [37, 466, 1.0], [37, 467, 1.0], [38, 455, 1.0], [38, 456, 1.0], [38, 457, 1.0], [38, 458, 1.0], [38, 459, 1.0], [38, 460, 1.0], [38, 461, 1.0], [38, 462, 1.0], [38, 463, 1.0], [38, 464, 1.0], [38, 465, 1.0], [38, 466, 1.0], [38, 467, 1.0], [39, 455, 1.0], [39, 456, 1.0], [39, 457, 1.0], [39, 458, 1.0], [39, 459, 1.0], [39, 460, 1.0], [39, 461, 1.0], [39, 462, 1.0], [39, 463, 1.0], [39, 464, 1.0], [39, 465, 1.0], [39, 466, 1.0], [39, 467, 1.0], [40, 455, 1.0], [40, 456, 1.0], [40, 457, 1.0], [40, 458, 1.0], [40, 459, 1.0], [40, 460, 1.0], [40, 461, 1.0], [40, 462, 1.0], [40, 463, 1.0], [40, 464, 1.0], [40, 465, 1.0], [40, 466, 1.0], [41, 455, 1.0], [41, 456, 1.0], [41, 457, 1.0], [41, 458, 1.0], [41, 459, 1.0], [41, 460, 1.0], [41, 461, 1.0], [41, 462, 1.0], [41, 463, 1.0], [41, 464, 1.0], [41, 465, 1.0], [41, 466, 1.0], [42, 455, 1.0], [42, 456, 1.0], [42, 457, 1.0], [42, 458, 1.0], ...] |
| 517473255 | 517473255 | [[41, 493, 1.0], [41, 494, 1.0], [41, 495, 1.0], [41, 496, 1.0], [42, 492, 1.0], [42, 493, 1.0], [42, 494, 1.0], [42, 495, 1.0], [42, 496, 1.0], [42, 497, 1.0], [43, 489, 1.0], [43, 490, 1.0], [43, 491, 1.0], [43, 492, 1.0], [43, 493, 1.0], [43, 494, 1.0], [43, 495, 1.0], [43, 496, 1.0], [43, 497, 1.0], [43, 498, 1.0], [44, 488, 1.0], [44, 489, 1.0], [44, 490, 1.0], [44, 491, 1.0], [44, 492, 1.0], [44, 493, 1.0], [44, 494, 1.0], [44, 495, 1.0], [44, 496, 1.0], [44, 497, 1.0], [44, 498, 1.0], [45, 487, 1.0], [45, 488, 1.0], [45, 489, 1.0], [45, 490, 1.0], [45, 491, 1.0], [45, 492, 1.0], [45, 493, 1.0], [45, 494, 1.0], [45, 495, 1.0], [45, 496, 1.0], [45, 497, 1.0], [45, 498, 1.0], [46, 486, 1.0], [46, 487, 1.0], [46, 488, 1.0], [46, 489, 1.0], [46, 490, 1.0], [46, 491, 1.0], [46, 492, 1.0], [46, 493, 1.0], [46, 494, 1.0], [46, 495, 1.0], [46, 496, 1.0], [46, 497, 1.0], [46, 498, 1.0], [47, 486, 1.0], [47, 487, 1.0], [47, 488, 1.0], [47, 489, 1.0], [47, 490, 1.0], [47, 491, 1.0], [47, 492, 1.0], [47, 493, 1.0], [47, 494, 1.0], [47, 495, 1.0], [47, 496, 1.0], [47, 497, 1.0], [47, 498, 1.0], [47, 499, 1.0], [48, 486, 1.0], [48, 487, 1.0], [48, 488, 1.0], [48, 489, 1.0], [48, 490, 1.0], [48, 491, 1.0], [48, 492, 1.0], [48, 493, 1.0], [48, 494, 1.0], [48, 495, 1.0], [48, 496, 1.0], [48, 497, 1.0], [48, 498, 1.0], [48, 499, 1.0], [49, 486, 1.0], [49, 487, 1.0], [49, 488, 1.0], [49, 489, 1.0], [49, 490, 1.0], [49, 491, 1.0], [49, 492, 1.0], [49, 493, 1.0], [49, 494, 1.0], [49, 495, 1.0], [49, 496, 1.0], [49, 497, 1.0], [49, 498, 1.0], [49, 499, 1.0], [50, 486, 1.0], [50, 487, 1.0], ...] |
... and 170 more rows.
DfOverFEvents
data
timestamps (link to processing/ophys/Fluorescence/Corrected/timestamps)
rois
table
imaging_plane
optical_channel
0
device
grid_spacing
table
| global_roi_id | pixel_mask | |
|---|---|---|
| id | ||
| 517473350 | 517473350 | [[55, 291, 1.0], [55, 292, 1.0], [56, 290, 1.0], [56, 291, 1.0], [56, 292, 1.0], [56, 293, 1.0], [56, 294, 1.0], [56, 295, 1.0], [56, 296, 1.0], [57, 289, 1.0], [57, 290, 1.0], [57, 291, 1.0], [57, 292, 1.0], [57, 293, 1.0], [57, 294, 1.0], [57, 295, 1.0], [57, 296, 1.0], [57, 297, 1.0], [57, 298, 1.0], [58, 288, 1.0], [58, 289, 1.0], [58, 290, 1.0], [58, 291, 1.0], [58, 292, 1.0], [58, 293, 1.0], [58, 294, 1.0], [58, 295, 1.0], [58, 296, 1.0], [58, 297, 1.0], [58, 298, 1.0], [58, 299, 1.0], [59, 288, 1.0], [59, 289, 1.0], [59, 290, 1.0], [59, 291, 1.0], [59, 292, 1.0], [59, 293, 1.0], [59, 294, 1.0], [59, 295, 1.0], [59, 296, 1.0], [59, 297, 1.0], [59, 298, 1.0], [59, 299, 1.0], [60, 288, 1.0], [60, 289, 1.0], [60, 290, 1.0], [60, 291, 1.0], [60, 292, 1.0], [60, 293, 1.0], [60, 294, 1.0], [60, 295, 1.0], [60, 296, 1.0], [60, 297, 1.0], [60, 298, 1.0], [60, 299, 1.0], [61, 287, 1.0], [61, 288, 1.0], [61, 289, 1.0], [61, 290, 1.0], [61, 291, 1.0], [61, 292, 1.0], [61, 293, 1.0], [61, 294, 1.0], [61, 295, 1.0], [61, 296, 1.0], [61, 297, 1.0], [61, 298, 1.0], [61, 299, 1.0], [62, 287, 1.0], [62, 288, 1.0], [62, 289, 1.0], [62, 290, 1.0], [62, 291, 1.0], [62, 292, 1.0], [62, 293, 1.0], [62, 294, 1.0], [62, 295, 1.0], [62, 296, 1.0], [62, 297, 1.0], [62, 298, 1.0], [62, 299, 1.0], [63, 287, 1.0], [63, 288, 1.0], [63, 289, 1.0], [63, 290, 1.0], [63, 291, 1.0], [63, 292, 1.0], [63, 293, 1.0], [63, 294, 1.0], [63, 295, 1.0], [63, 296, 1.0], [63, 297, 1.0], [63, 298, 1.0], [63, 299, 1.0], [64, 287, 1.0], [64, 288, 1.0], [64, 289, 1.0], [64, 290, 1.0], [64, 291, 1.0], [64, 292, 1.0], ...] |
| 517473341 | 517473341 | [[58, 272, 1.0], [58, 273, 1.0], [58, 274, 1.0], [58, 275, 1.0], [58, 276, 1.0], [58, 277, 1.0], [59, 271, 1.0], [59, 272, 1.0], [59, 273, 1.0], [59, 274, 1.0], [59, 275, 1.0], [59, 276, 1.0], [59, 277, 1.0], [59, 278, 1.0], [59, 279, 1.0], [59, 280, 1.0], [59, 281, 1.0], [59, 282, 1.0], [60, 271, 1.0], [60, 272, 1.0], [60, 273, 1.0], [60, 274, 1.0], [60, 275, 1.0], [60, 276, 1.0], [60, 277, 1.0], [60, 278, 1.0], [60, 279, 1.0], [60, 280, 1.0], [60, 281, 1.0], [60, 282, 1.0], [60, 283, 1.0], [61, 270, 1.0], [61, 271, 1.0], [61, 272, 1.0], [61, 273, 1.0], [61, 274, 1.0], [61, 275, 1.0], [61, 276, 1.0], [61, 277, 1.0], [61, 278, 1.0], [61, 279, 1.0], [61, 280, 1.0], [61, 281, 1.0], [61, 282, 1.0], [62, 270, 1.0], [62, 271, 1.0], [62, 272, 1.0], [62, 273, 1.0], [62, 274, 1.0], [62, 275, 1.0], [62, 276, 1.0], [62, 277, 1.0], [62, 278, 1.0], [62, 279, 1.0], [62, 280, 1.0], [62, 281, 1.0], [62, 282, 1.0], [63, 269, 1.0], [63, 270, 1.0], [63, 271, 1.0], [63, 272, 1.0], [63, 273, 1.0], [63, 274, 1.0], [63, 275, 1.0], [63, 276, 1.0], [63, 277, 1.0], [63, 278, 1.0], [63, 279, 1.0], [63, 280, 1.0], [63, 281, 1.0], [63, 282, 1.0], [64, 268, 1.0], [64, 269, 1.0], [64, 270, 1.0], [64, 271, 1.0], [64, 272, 1.0], [64, 273, 1.0], [64, 274, 1.0], [64, 275, 1.0], [64, 276, 1.0], [64, 277, 1.0], [64, 278, 1.0], [64, 279, 1.0], [64, 280, 1.0], [64, 281, 1.0], [64, 282, 1.0], [65, 268, 1.0], [65, 269, 1.0], [65, 270, 1.0], [65, 271, 1.0], [65, 272, 1.0], [65, 273, 1.0], [65, 274, 1.0], [65, 275, 1.0], [65, 276, 1.0], [65, 277, 1.0], [65, 278, 1.0], [65, 279, 1.0], [65, 280, 1.0], [65, 281, 1.0], ...] |
| 517473313 | 517473313 | [[33, 459, 1.0], [33, 460, 1.0], [33, 461, 1.0], [33, 462, 1.0], [33, 463, 1.0], [34, 458, 1.0], [34, 459, 1.0], [34, 460, 1.0], [34, 461, 1.0], [34, 462, 1.0], [34, 463, 1.0], [34, 464, 1.0], [35, 457, 1.0], [35, 458, 1.0], [35, 459, 1.0], [35, 460, 1.0], [35, 461, 1.0], [35, 462, 1.0], [35, 463, 1.0], [35, 464, 1.0], [35, 465, 1.0], [35, 466, 1.0], [36, 456, 1.0], [36, 457, 1.0], [36, 458, 1.0], [36, 459, 1.0], [36, 460, 1.0], [36, 461, 1.0], [36, 462, 1.0], [36, 463, 1.0], [36, 464, 1.0], [36, 465, 1.0], [36, 466, 1.0], [36, 467, 1.0], [37, 456, 1.0], [37, 457, 1.0], [37, 458, 1.0], [37, 459, 1.0], [37, 460, 1.0], [37, 461, 1.0], [37, 462, 1.0], [37, 463, 1.0], [37, 464, 1.0], [37, 465, 1.0], [37, 466, 1.0], [37, 467, 1.0], [38, 455, 1.0], [38, 456, 1.0], [38, 457, 1.0], [38, 458, 1.0], [38, 459, 1.0], [38, 460, 1.0], [38, 461, 1.0], [38, 462, 1.0], [38, 463, 1.0], [38, 464, 1.0], [38, 465, 1.0], [38, 466, 1.0], [38, 467, 1.0], [39, 455, 1.0], [39, 456, 1.0], [39, 457, 1.0], [39, 458, 1.0], [39, 459, 1.0], [39, 460, 1.0], [39, 461, 1.0], [39, 462, 1.0], [39, 463, 1.0], [39, 464, 1.0], [39, 465, 1.0], [39, 466, 1.0], [39, 467, 1.0], [40, 455, 1.0], [40, 456, 1.0], [40, 457, 1.0], [40, 458, 1.0], [40, 459, 1.0], [40, 460, 1.0], [40, 461, 1.0], [40, 462, 1.0], [40, 463, 1.0], [40, 464, 1.0], [40, 465, 1.0], [40, 466, 1.0], [41, 455, 1.0], [41, 456, 1.0], [41, 457, 1.0], [41, 458, 1.0], [41, 459, 1.0], [41, 460, 1.0], [41, 461, 1.0], [41, 462, 1.0], [41, 463, 1.0], [41, 464, 1.0], [41, 465, 1.0], [41, 466, 1.0], [42, 455, 1.0], [42, 456, 1.0], [42, 457, 1.0], [42, 458, 1.0], ...] |
| 517473255 | 517473255 | [[41, 493, 1.0], [41, 494, 1.0], [41, 495, 1.0], [41, 496, 1.0], [42, 492, 1.0], [42, 493, 1.0], [42, 494, 1.0], [42, 495, 1.0], [42, 496, 1.0], [42, 497, 1.0], [43, 489, 1.0], [43, 490, 1.0], [43, 491, 1.0], [43, 492, 1.0], [43, 493, 1.0], [43, 494, 1.0], [43, 495, 1.0], [43, 496, 1.0], [43, 497, 1.0], [43, 498, 1.0], [44, 488, 1.0], [44, 489, 1.0], [44, 490, 1.0], [44, 491, 1.0], [44, 492, 1.0], [44, 493, 1.0], [44, 494, 1.0], [44, 495, 1.0], [44, 496, 1.0], [44, 497, 1.0], [44, 498, 1.0], [45, 487, 1.0], [45, 488, 1.0], [45, 489, 1.0], [45, 490, 1.0], [45, 491, 1.0], [45, 492, 1.0], [45, 493, 1.0], [45, 494, 1.0], [45, 495, 1.0], [45, 496, 1.0], [45, 497, 1.0], [45, 498, 1.0], [46, 486, 1.0], [46, 487, 1.0], [46, 488, 1.0], [46, 489, 1.0], [46, 490, 1.0], [46, 491, 1.0], [46, 492, 1.0], [46, 493, 1.0], [46, 494, 1.0], [46, 495, 1.0], [46, 496, 1.0], [46, 497, 1.0], [46, 498, 1.0], [47, 486, 1.0], [47, 487, 1.0], [47, 488, 1.0], [47, 489, 1.0], [47, 490, 1.0], [47, 491, 1.0], [47, 492, 1.0], [47, 493, 1.0], [47, 494, 1.0], [47, 495, 1.0], [47, 496, 1.0], [47, 497, 1.0], [47, 498, 1.0], [47, 499, 1.0], [48, 486, 1.0], [48, 487, 1.0], [48, 488, 1.0], [48, 489, 1.0], [48, 490, 1.0], [48, 491, 1.0], [48, 492, 1.0], [48, 493, 1.0], [48, 494, 1.0], [48, 495, 1.0], [48, 496, 1.0], [48, 497, 1.0], [48, 498, 1.0], [48, 499, 1.0], [49, 486, 1.0], [49, 487, 1.0], [49, 488, 1.0], [49, 489, 1.0], [49, 490, 1.0], [49, 491, 1.0], [49, 492, 1.0], [49, 493, 1.0], [49, 494, 1.0], [49, 495, 1.0], [49, 496, 1.0], [49, 497, 1.0], [49, 498, 1.0], [49, 499, 1.0], [50, 486, 1.0], [50, 487, 1.0], ...] |
... and 170 more rows.
nwbfile.processing["ophys"]["Fluorescence"]
Fluorescence
roi_response_series
Corrected
data
timestamps
rois
table
imaging_plane
optical_channel
0
device
grid_spacing
table
| global_roi_id | pixel_mask | |
|---|---|---|
| id | ||
| 517473350 | 517473350 | [[55, 291, 1.0], [55, 292, 1.0], [56, 290, 1.0], [56, 291, 1.0], [56, 292, 1.0], [56, 293, 1.0], [56, 294, 1.0], [56, 295, 1.0], [56, 296, 1.0], [57, 289, 1.0], [57, 290, 1.0], [57, 291, 1.0], [57, 292, 1.0], [57, 293, 1.0], [57, 294, 1.0], [57, 295, 1.0], [57, 296, 1.0], [57, 297, 1.0], [57, 298, 1.0], [58, 288, 1.0], [58, 289, 1.0], [58, 290, 1.0], [58, 291, 1.0], [58, 292, 1.0], [58, 293, 1.0], [58, 294, 1.0], [58, 295, 1.0], [58, 296, 1.0], [58, 297, 1.0], [58, 298, 1.0], [58, 299, 1.0], [59, 288, 1.0], [59, 289, 1.0], [59, 290, 1.0], [59, 291, 1.0], [59, 292, 1.0], [59, 293, 1.0], [59, 294, 1.0], [59, 295, 1.0], [59, 296, 1.0], [59, 297, 1.0], [59, 298, 1.0], [59, 299, 1.0], [60, 288, 1.0], [60, 289, 1.0], [60, 290, 1.0], [60, 291, 1.0], [60, 292, 1.0], [60, 293, 1.0], [60, 294, 1.0], [60, 295, 1.0], [60, 296, 1.0], [60, 297, 1.0], [60, 298, 1.0], [60, 299, 1.0], [61, 287, 1.0], [61, 288, 1.0], [61, 289, 1.0], [61, 290, 1.0], [61, 291, 1.0], [61, 292, 1.0], [61, 293, 1.0], [61, 294, 1.0], [61, 295, 1.0], [61, 296, 1.0], [61, 297, 1.0], [61, 298, 1.0], [61, 299, 1.0], [62, 287, 1.0], [62, 288, 1.0], [62, 289, 1.0], [62, 290, 1.0], [62, 291, 1.0], [62, 292, 1.0], [62, 293, 1.0], [62, 294, 1.0], [62, 295, 1.0], [62, 296, 1.0], [62, 297, 1.0], [62, 298, 1.0], [62, 299, 1.0], [63, 287, 1.0], [63, 288, 1.0], [63, 289, 1.0], [63, 290, 1.0], [63, 291, 1.0], [63, 292, 1.0], [63, 293, 1.0], [63, 294, 1.0], [63, 295, 1.0], [63, 296, 1.0], [63, 297, 1.0], [63, 298, 1.0], [63, 299, 1.0], [64, 287, 1.0], [64, 288, 1.0], [64, 289, 1.0], [64, 290, 1.0], [64, 291, 1.0], [64, 292, 1.0], ...] |
| 517473341 | 517473341 | [[58, 272, 1.0], [58, 273, 1.0], [58, 274, 1.0], [58, 275, 1.0], [58, 276, 1.0], [58, 277, 1.0], [59, 271, 1.0], [59, 272, 1.0], [59, 273, 1.0], [59, 274, 1.0], [59, 275, 1.0], [59, 276, 1.0], [59, 277, 1.0], [59, 278, 1.0], [59, 279, 1.0], [59, 280, 1.0], [59, 281, 1.0], [59, 282, 1.0], [60, 271, 1.0], [60, 272, 1.0], [60, 273, 1.0], [60, 274, 1.0], [60, 275, 1.0], [60, 276, 1.0], [60, 277, 1.0], [60, 278, 1.0], [60, 279, 1.0], [60, 280, 1.0], [60, 281, 1.0], [60, 282, 1.0], [60, 283, 1.0], [61, 270, 1.0], [61, 271, 1.0], [61, 272, 1.0], [61, 273, 1.0], [61, 274, 1.0], [61, 275, 1.0], [61, 276, 1.0], [61, 277, 1.0], [61, 278, 1.0], [61, 279, 1.0], [61, 280, 1.0], [61, 281, 1.0], [61, 282, 1.0], [62, 270, 1.0], [62, 271, 1.0], [62, 272, 1.0], [62, 273, 1.0], [62, 274, 1.0], [62, 275, 1.0], [62, 276, 1.0], [62, 277, 1.0], [62, 278, 1.0], [62, 279, 1.0], [62, 280, 1.0], [62, 281, 1.0], [62, 282, 1.0], [63, 269, 1.0], [63, 270, 1.0], [63, 271, 1.0], [63, 272, 1.0], [63, 273, 1.0], [63, 274, 1.0], [63, 275, 1.0], [63, 276, 1.0], [63, 277, 1.0], [63, 278, 1.0], [63, 279, 1.0], [63, 280, 1.0], [63, 281, 1.0], [63, 282, 1.0], [64, 268, 1.0], [64, 269, 1.0], [64, 270, 1.0], [64, 271, 1.0], [64, 272, 1.0], [64, 273, 1.0], [64, 274, 1.0], [64, 275, 1.0], [64, 276, 1.0], [64, 277, 1.0], [64, 278, 1.0], [64, 279, 1.0], [64, 280, 1.0], [64, 281, 1.0], [64, 282, 1.0], [65, 268, 1.0], [65, 269, 1.0], [65, 270, 1.0], [65, 271, 1.0], [65, 272, 1.0], [65, 273, 1.0], [65, 274, 1.0], [65, 275, 1.0], [65, 276, 1.0], [65, 277, 1.0], [65, 278, 1.0], [65, 279, 1.0], [65, 280, 1.0], [65, 281, 1.0], ...] |
| 517473313 | 517473313 | [[33, 459, 1.0], [33, 460, 1.0], [33, 461, 1.0], [33, 462, 1.0], [33, 463, 1.0], [34, 458, 1.0], [34, 459, 1.0], [34, 460, 1.0], [34, 461, 1.0], [34, 462, 1.0], [34, 463, 1.0], [34, 464, 1.0], [35, 457, 1.0], [35, 458, 1.0], [35, 459, 1.0], [35, 460, 1.0], [35, 461, 1.0], [35, 462, 1.0], [35, 463, 1.0], [35, 464, 1.0], [35, 465, 1.0], [35, 466, 1.0], [36, 456, 1.0], [36, 457, 1.0], [36, 458, 1.0], [36, 459, 1.0], [36, 460, 1.0], [36, 461, 1.0], [36, 462, 1.0], [36, 463, 1.0], [36, 464, 1.0], [36, 465, 1.0], [36, 466, 1.0], [36, 467, 1.0], [37, 456, 1.0], [37, 457, 1.0], [37, 458, 1.0], [37, 459, 1.0], [37, 460, 1.0], [37, 461, 1.0], [37, 462, 1.0], [37, 463, 1.0], [37, 464, 1.0], [37, 465, 1.0], [37, 466, 1.0], [37, 467, 1.0], [38, 455, 1.0], [38, 456, 1.0], [38, 457, 1.0], [38, 458, 1.0], [38, 459, 1.0], [38, 460, 1.0], [38, 461, 1.0], [38, 462, 1.0], [38, 463, 1.0], [38, 464, 1.0], [38, 465, 1.0], [38, 466, 1.0], [38, 467, 1.0], [39, 455, 1.0], [39, 456, 1.0], [39, 457, 1.0], [39, 458, 1.0], [39, 459, 1.0], [39, 460, 1.0], [39, 461, 1.0], [39, 462, 1.0], [39, 463, 1.0], [39, 464, 1.0], [39, 465, 1.0], [39, 466, 1.0], [39, 467, 1.0], [40, 455, 1.0], [40, 456, 1.0], [40, 457, 1.0], [40, 458, 1.0], [40, 459, 1.0], [40, 460, 1.0], [40, 461, 1.0], [40, 462, 1.0], [40, 463, 1.0], [40, 464, 1.0], [40, 465, 1.0], [40, 466, 1.0], [41, 455, 1.0], [41, 456, 1.0], [41, 457, 1.0], [41, 458, 1.0], [41, 459, 1.0], [41, 460, 1.0], [41, 461, 1.0], [41, 462, 1.0], [41, 463, 1.0], [41, 464, 1.0], [41, 465, 1.0], [41, 466, 1.0], [42, 455, 1.0], [42, 456, 1.0], [42, 457, 1.0], [42, 458, 1.0], ...] |
| 517473255 | 517473255 | [[41, 493, 1.0], [41, 494, 1.0], [41, 495, 1.0], [41, 496, 1.0], [42, 492, 1.0], [42, 493, 1.0], [42, 494, 1.0], [42, 495, 1.0], [42, 496, 1.0], [42, 497, 1.0], [43, 489, 1.0], [43, 490, 1.0], [43, 491, 1.0], [43, 492, 1.0], [43, 493, 1.0], [43, 494, 1.0], [43, 495, 1.0], [43, 496, 1.0], [43, 497, 1.0], [43, 498, 1.0], [44, 488, 1.0], [44, 489, 1.0], [44, 490, 1.0], [44, 491, 1.0], [44, 492, 1.0], [44, 493, 1.0], [44, 494, 1.0], [44, 495, 1.0], [44, 496, 1.0], [44, 497, 1.0], [44, 498, 1.0], [45, 487, 1.0], [45, 488, 1.0], [45, 489, 1.0], [45, 490, 1.0], [45, 491, 1.0], [45, 492, 1.0], [45, 493, 1.0], [45, 494, 1.0], [45, 495, 1.0], [45, 496, 1.0], [45, 497, 1.0], [45, 498, 1.0], [46, 486, 1.0], [46, 487, 1.0], [46, 488, 1.0], [46, 489, 1.0], [46, 490, 1.0], [46, 491, 1.0], [46, 492, 1.0], [46, 493, 1.0], [46, 494, 1.0], [46, 495, 1.0], [46, 496, 1.0], [46, 497, 1.0], [46, 498, 1.0], [47, 486, 1.0], [47, 487, 1.0], [47, 488, 1.0], [47, 489, 1.0], [47, 490, 1.0], [47, 491, 1.0], [47, 492, 1.0], [47, 493, 1.0], [47, 494, 1.0], [47, 495, 1.0], [47, 496, 1.0], [47, 497, 1.0], [47, 498, 1.0], [47, 499, 1.0], [48, 486, 1.0], [48, 487, 1.0], [48, 488, 1.0], [48, 489, 1.0], [48, 490, 1.0], [48, 491, 1.0], [48, 492, 1.0], [48, 493, 1.0], [48, 494, 1.0], [48, 495, 1.0], [48, 496, 1.0], [48, 497, 1.0], [48, 498, 1.0], [48, 499, 1.0], [49, 486, 1.0], [49, 487, 1.0], [49, 488, 1.0], [49, 489, 1.0], [49, 490, 1.0], [49, 491, 1.0], [49, 492, 1.0], [49, 493, 1.0], [49, 494, 1.0], [49, 495, 1.0], [49, 496, 1.0], [49, 497, 1.0], [49, 498, 1.0], [49, 499, 1.0], [50, 486, 1.0], [50, 487, 1.0], ...] |
... and 170 more rows.
timestamp_link
Demixed
data
timestamps (link to processing/ophys/Fluorescence/Corrected/timestamps)
rois
table
imaging_plane
optical_channel
0
device
grid_spacing
table
| global_roi_id | pixel_mask | |
|---|---|---|
| id | ||
| 517473350 | 517473350 | [[55, 291, 1.0], [55, 292, 1.0], [56, 290, 1.0], [56, 291, 1.0], [56, 292, 1.0], [56, 293, 1.0], [56, 294, 1.0], [56, 295, 1.0], [56, 296, 1.0], [57, 289, 1.0], [57, 290, 1.0], [57, 291, 1.0], [57, 292, 1.0], [57, 293, 1.0], [57, 294, 1.0], [57, 295, 1.0], [57, 296, 1.0], [57, 297, 1.0], [57, 298, 1.0], [58, 288, 1.0], [58, 289, 1.0], [58, 290, 1.0], [58, 291, 1.0], [58, 292, 1.0], [58, 293, 1.0], [58, 294, 1.0], [58, 295, 1.0], [58, 296, 1.0], [58, 297, 1.0], [58, 298, 1.0], [58, 299, 1.0], [59, 288, 1.0], [59, 289, 1.0], [59, 290, 1.0], [59, 291, 1.0], [59, 292, 1.0], [59, 293, 1.0], [59, 294, 1.0], [59, 295, 1.0], [59, 296, 1.0], [59, 297, 1.0], [59, 298, 1.0], [59, 299, 1.0], [60, 288, 1.0], [60, 289, 1.0], [60, 290, 1.0], [60, 291, 1.0], [60, 292, 1.0], [60, 293, 1.0], [60, 294, 1.0], [60, 295, 1.0], [60, 296, 1.0], [60, 297, 1.0], [60, 298, 1.0], [60, 299, 1.0], [61, 287, 1.0], [61, 288, 1.0], [61, 289, 1.0], [61, 290, 1.0], [61, 291, 1.0], [61, 292, 1.0], [61, 293, 1.0], [61, 294, 1.0], [61, 295, 1.0], [61, 296, 1.0], [61, 297, 1.0], [61, 298, 1.0], [61, 299, 1.0], [62, 287, 1.0], [62, 288, 1.0], [62, 289, 1.0], [62, 290, 1.0], [62, 291, 1.0], [62, 292, 1.0], [62, 293, 1.0], [62, 294, 1.0], [62, 295, 1.0], [62, 296, 1.0], [62, 297, 1.0], [62, 298, 1.0], [62, 299, 1.0], [63, 287, 1.0], [63, 288, 1.0], [63, 289, 1.0], [63, 290, 1.0], [63, 291, 1.0], [63, 292, 1.0], [63, 293, 1.0], [63, 294, 1.0], [63, 295, 1.0], [63, 296, 1.0], [63, 297, 1.0], [63, 298, 1.0], [63, 299, 1.0], [64, 287, 1.0], [64, 288, 1.0], [64, 289, 1.0], [64, 290, 1.0], [64, 291, 1.0], [64, 292, 1.0], ...] |
| 517473341 | 517473341 | [[58, 272, 1.0], [58, 273, 1.0], [58, 274, 1.0], [58, 275, 1.0], [58, 276, 1.0], [58, 277, 1.0], [59, 271, 1.0], [59, 272, 1.0], [59, 273, 1.0], [59, 274, 1.0], [59, 275, 1.0], [59, 276, 1.0], [59, 277, 1.0], [59, 278, 1.0], [59, 279, 1.0], [59, 280, 1.0], [59, 281, 1.0], [59, 282, 1.0], [60, 271, 1.0], [60, 272, 1.0], [60, 273, 1.0], [60, 274, 1.0], [60, 275, 1.0], [60, 276, 1.0], [60, 277, 1.0], [60, 278, 1.0], [60, 279, 1.0], [60, 280, 1.0], [60, 281, 1.0], [60, 282, 1.0], [60, 283, 1.0], [61, 270, 1.0], [61, 271, 1.0], [61, 272, 1.0], [61, 273, 1.0], [61, 274, 1.0], [61, 275, 1.0], [61, 276, 1.0], [61, 277, 1.0], [61, 278, 1.0], [61, 279, 1.0], [61, 280, 1.0], [61, 281, 1.0], [61, 282, 1.0], [62, 270, 1.0], [62, 271, 1.0], [62, 272, 1.0], [62, 273, 1.0], [62, 274, 1.0], [62, 275, 1.0], [62, 276, 1.0], [62, 277, 1.0], [62, 278, 1.0], [62, 279, 1.0], [62, 280, 1.0], [62, 281, 1.0], [62, 282, 1.0], [63, 269, 1.0], [63, 270, 1.0], [63, 271, 1.0], [63, 272, 1.0], [63, 273, 1.0], [63, 274, 1.0], [63, 275, 1.0], [63, 276, 1.0], [63, 277, 1.0], [63, 278, 1.0], [63, 279, 1.0], [63, 280, 1.0], [63, 281, 1.0], [63, 282, 1.0], [64, 268, 1.0], [64, 269, 1.0], [64, 270, 1.0], [64, 271, 1.0], [64, 272, 1.0], [64, 273, 1.0], [64, 274, 1.0], [64, 275, 1.0], [64, 276, 1.0], [64, 277, 1.0], [64, 278, 1.0], [64, 279, 1.0], [64, 280, 1.0], [64, 281, 1.0], [64, 282, 1.0], [65, 268, 1.0], [65, 269, 1.0], [65, 270, 1.0], [65, 271, 1.0], [65, 272, 1.0], [65, 273, 1.0], [65, 274, 1.0], [65, 275, 1.0], [65, 276, 1.0], [65, 277, 1.0], [65, 278, 1.0], [65, 279, 1.0], [65, 280, 1.0], [65, 281, 1.0], ...] |
| 517473313 | 517473313 | [[33, 459, 1.0], [33, 460, 1.0], [33, 461, 1.0], [33, 462, 1.0], [33, 463, 1.0], [34, 458, 1.0], [34, 459, 1.0], [34, 460, 1.0], [34, 461, 1.0], [34, 462, 1.0], [34, 463, 1.0], [34, 464, 1.0], [35, 457, 1.0], [35, 458, 1.0], [35, 459, 1.0], [35, 460, 1.0], [35, 461, 1.0], [35, 462, 1.0], [35, 463, 1.0], [35, 464, 1.0], [35, 465, 1.0], [35, 466, 1.0], [36, 456, 1.0], [36, 457, 1.0], [36, 458, 1.0], [36, 459, 1.0], [36, 460, 1.0], [36, 461, 1.0], [36, 462, 1.0], [36, 463, 1.0], [36, 464, 1.0], [36, 465, 1.0], [36, 466, 1.0], [36, 467, 1.0], [37, 456, 1.0], [37, 457, 1.0], [37, 458, 1.0], [37, 459, 1.0], [37, 460, 1.0], [37, 461, 1.0], [37, 462, 1.0], [37, 463, 1.0], [37, 464, 1.0], [37, 465, 1.0], [37, 466, 1.0], [37, 467, 1.0], [38, 455, 1.0], [38, 456, 1.0], [38, 457, 1.0], [38, 458, 1.0], [38, 459, 1.0], [38, 460, 1.0], [38, 461, 1.0], [38, 462, 1.0], [38, 463, 1.0], [38, 464, 1.0], [38, 465, 1.0], [38, 466, 1.0], [38, 467, 1.0], [39, 455, 1.0], [39, 456, 1.0], [39, 457, 1.0], [39, 458, 1.0], [39, 459, 1.0], [39, 460, 1.0], [39, 461, 1.0], [39, 462, 1.0], [39, 463, 1.0], [39, 464, 1.0], [39, 465, 1.0], [39, 466, 1.0], [39, 467, 1.0], [40, 455, 1.0], [40, 456, 1.0], [40, 457, 1.0], [40, 458, 1.0], [40, 459, 1.0], [40, 460, 1.0], [40, 461, 1.0], [40, 462, 1.0], [40, 463, 1.0], [40, 464, 1.0], [40, 465, 1.0], [40, 466, 1.0], [41, 455, 1.0], [41, 456, 1.0], [41, 457, 1.0], [41, 458, 1.0], [41, 459, 1.0], [41, 460, 1.0], [41, 461, 1.0], [41, 462, 1.0], [41, 463, 1.0], [41, 464, 1.0], [41, 465, 1.0], [41, 466, 1.0], [42, 455, 1.0], [42, 456, 1.0], [42, 457, 1.0], [42, 458, 1.0], ...] |
| 517473255 | 517473255 | [[41, 493, 1.0], [41, 494, 1.0], [41, 495, 1.0], [41, 496, 1.0], [42, 492, 1.0], [42, 493, 1.0], [42, 494, 1.0], [42, 495, 1.0], [42, 496, 1.0], [42, 497, 1.0], [43, 489, 1.0], [43, 490, 1.0], [43, 491, 1.0], [43, 492, 1.0], [43, 493, 1.0], [43, 494, 1.0], [43, 495, 1.0], [43, 496, 1.0], [43, 497, 1.0], [43, 498, 1.0], [44, 488, 1.0], [44, 489, 1.0], [44, 490, 1.0], [44, 491, 1.0], [44, 492, 1.0], [44, 493, 1.0], [44, 494, 1.0], [44, 495, 1.0], [44, 496, 1.0], [44, 497, 1.0], [44, 498, 1.0], [45, 487, 1.0], [45, 488, 1.0], [45, 489, 1.0], [45, 490, 1.0], [45, 491, 1.0], [45, 492, 1.0], [45, 493, 1.0], [45, 494, 1.0], [45, 495, 1.0], [45, 496, 1.0], [45, 497, 1.0], [45, 498, 1.0], [46, 486, 1.0], [46, 487, 1.0], [46, 488, 1.0], [46, 489, 1.0], [46, 490, 1.0], [46, 491, 1.0], [46, 492, 1.0], [46, 493, 1.0], [46, 494, 1.0], [46, 495, 1.0], [46, 496, 1.0], [46, 497, 1.0], [46, 498, 1.0], [47, 486, 1.0], [47, 487, 1.0], [47, 488, 1.0], [47, 489, 1.0], [47, 490, 1.0], [47, 491, 1.0], [47, 492, 1.0], [47, 493, 1.0], [47, 494, 1.0], [47, 495, 1.0], [47, 496, 1.0], [47, 497, 1.0], [47, 498, 1.0], [47, 499, 1.0], [48, 486, 1.0], [48, 487, 1.0], [48, 488, 1.0], [48, 489, 1.0], [48, 490, 1.0], [48, 491, 1.0], [48, 492, 1.0], [48, 493, 1.0], [48, 494, 1.0], [48, 495, 1.0], [48, 496, 1.0], [48, 497, 1.0], [48, 498, 1.0], [48, 499, 1.0], [49, 486, 1.0], [49, 487, 1.0], [49, 488, 1.0], [49, 489, 1.0], [49, 490, 1.0], [49, 491, 1.0], [49, 492, 1.0], [49, 493, 1.0], [49, 494, 1.0], [49, 495, 1.0], [49, 496, 1.0], [49, 497, 1.0], [49, 498, 1.0], [49, 499, 1.0], [50, 486, 1.0], [50, 487, 1.0], ...] |
... and 170 more rows.
Neuropil
data
timestamps (link to processing/ophys/Fluorescence/Corrected/timestamps)
rois
table
imaging_plane
optical_channel
0
device
grid_spacing
table
| global_roi_id | pixel_mask | |
|---|---|---|
| id | ||
| 517473350 | 517473350 | [[55, 291, 1.0], [55, 292, 1.0], [56, 290, 1.0], [56, 291, 1.0], [56, 292, 1.0], [56, 293, 1.0], [56, 294, 1.0], [56, 295, 1.0], [56, 296, 1.0], [57, 289, 1.0], [57, 290, 1.0], [57, 291, 1.0], [57, 292, 1.0], [57, 293, 1.0], [57, 294, 1.0], [57, 295, 1.0], [57, 296, 1.0], [57, 297, 1.0], [57, 298, 1.0], [58, 288, 1.0], [58, 289, 1.0], [58, 290, 1.0], [58, 291, 1.0], [58, 292, 1.0], [58, 293, 1.0], [58, 294, 1.0], [58, 295, 1.0], [58, 296, 1.0], [58, 297, 1.0], [58, 298, 1.0], [58, 299, 1.0], [59, 288, 1.0], [59, 289, 1.0], [59, 290, 1.0], [59, 291, 1.0], [59, 292, 1.0], [59, 293, 1.0], [59, 294, 1.0], [59, 295, 1.0], [59, 296, 1.0], [59, 297, 1.0], [59, 298, 1.0], [59, 299, 1.0], [60, 288, 1.0], [60, 289, 1.0], [60, 290, 1.0], [60, 291, 1.0], [60, 292, 1.0], [60, 293, 1.0], [60, 294, 1.0], [60, 295, 1.0], [60, 296, 1.0], [60, 297, 1.0], [60, 298, 1.0], [60, 299, 1.0], [61, 287, 1.0], [61, 288, 1.0], [61, 289, 1.0], [61, 290, 1.0], [61, 291, 1.0], [61, 292, 1.0], [61, 293, 1.0], [61, 294, 1.0], [61, 295, 1.0], [61, 296, 1.0], [61, 297, 1.0], [61, 298, 1.0], [61, 299, 1.0], [62, 287, 1.0], [62, 288, 1.0], [62, 289, 1.0], [62, 290, 1.0], [62, 291, 1.0], [62, 292, 1.0], [62, 293, 1.0], [62, 294, 1.0], [62, 295, 1.0], [62, 296, 1.0], [62, 297, 1.0], [62, 298, 1.0], [62, 299, 1.0], [63, 287, 1.0], [63, 288, 1.0], [63, 289, 1.0], [63, 290, 1.0], [63, 291, 1.0], [63, 292, 1.0], [63, 293, 1.0], [63, 294, 1.0], [63, 295, 1.0], [63, 296, 1.0], [63, 297, 1.0], [63, 298, 1.0], [63, 299, 1.0], [64, 287, 1.0], [64, 288, 1.0], [64, 289, 1.0], [64, 290, 1.0], [64, 291, 1.0], [64, 292, 1.0], ...] |
| 517473341 | 517473341 | [[58, 272, 1.0], [58, 273, 1.0], [58, 274, 1.0], [58, 275, 1.0], [58, 276, 1.0], [58, 277, 1.0], [59, 271, 1.0], [59, 272, 1.0], [59, 273, 1.0], [59, 274, 1.0], [59, 275, 1.0], [59, 276, 1.0], [59, 277, 1.0], [59, 278, 1.0], [59, 279, 1.0], [59, 280, 1.0], [59, 281, 1.0], [59, 282, 1.0], [60, 271, 1.0], [60, 272, 1.0], [60, 273, 1.0], [60, 274, 1.0], [60, 275, 1.0], [60, 276, 1.0], [60, 277, 1.0], [60, 278, 1.0], [60, 279, 1.0], [60, 280, 1.0], [60, 281, 1.0], [60, 282, 1.0], [60, 283, 1.0], [61, 270, 1.0], [61, 271, 1.0], [61, 272, 1.0], [61, 273, 1.0], [61, 274, 1.0], [61, 275, 1.0], [61, 276, 1.0], [61, 277, 1.0], [61, 278, 1.0], [61, 279, 1.0], [61, 280, 1.0], [61, 281, 1.0], [61, 282, 1.0], [62, 270, 1.0], [62, 271, 1.0], [62, 272, 1.0], [62, 273, 1.0], [62, 274, 1.0], [62, 275, 1.0], [62, 276, 1.0], [62, 277, 1.0], [62, 278, 1.0], [62, 279, 1.0], [62, 280, 1.0], [62, 281, 1.0], [62, 282, 1.0], [63, 269, 1.0], [63, 270, 1.0], [63, 271, 1.0], [63, 272, 1.0], [63, 273, 1.0], [63, 274, 1.0], [63, 275, 1.0], [63, 276, 1.0], [63, 277, 1.0], [63, 278, 1.0], [63, 279, 1.0], [63, 280, 1.0], [63, 281, 1.0], [63, 282, 1.0], [64, 268, 1.0], [64, 269, 1.0], [64, 270, 1.0], [64, 271, 1.0], [64, 272, 1.0], [64, 273, 1.0], [64, 274, 1.0], [64, 275, 1.0], [64, 276, 1.0], [64, 277, 1.0], [64, 278, 1.0], [64, 279, 1.0], [64, 280, 1.0], [64, 281, 1.0], [64, 282, 1.0], [65, 268, 1.0], [65, 269, 1.0], [65, 270, 1.0], [65, 271, 1.0], [65, 272, 1.0], [65, 273, 1.0], [65, 274, 1.0], [65, 275, 1.0], [65, 276, 1.0], [65, 277, 1.0], [65, 278, 1.0], [65, 279, 1.0], [65, 280, 1.0], [65, 281, 1.0], ...] |
| 517473313 | 517473313 | [[33, 459, 1.0], [33, 460, 1.0], [33, 461, 1.0], [33, 462, 1.0], [33, 463, 1.0], [34, 458, 1.0], [34, 459, 1.0], [34, 460, 1.0], [34, 461, 1.0], [34, 462, 1.0], [34, 463, 1.0], [34, 464, 1.0], [35, 457, 1.0], [35, 458, 1.0], [35, 459, 1.0], [35, 460, 1.0], [35, 461, 1.0], [35, 462, 1.0], [35, 463, 1.0], [35, 464, 1.0], [35, 465, 1.0], [35, 466, 1.0], [36, 456, 1.0], [36, 457, 1.0], [36, 458, 1.0], [36, 459, 1.0], [36, 460, 1.0], [36, 461, 1.0], [36, 462, 1.0], [36, 463, 1.0], [36, 464, 1.0], [36, 465, 1.0], [36, 466, 1.0], [36, 467, 1.0], [37, 456, 1.0], [37, 457, 1.0], [37, 458, 1.0], [37, 459, 1.0], [37, 460, 1.0], [37, 461, 1.0], [37, 462, 1.0], [37, 463, 1.0], [37, 464, 1.0], [37, 465, 1.0], [37, 466, 1.0], [37, 467, 1.0], [38, 455, 1.0], [38, 456, 1.0], [38, 457, 1.0], [38, 458, 1.0], [38, 459, 1.0], [38, 460, 1.0], [38, 461, 1.0], [38, 462, 1.0], [38, 463, 1.0], [38, 464, 1.0], [38, 465, 1.0], [38, 466, 1.0], [38, 467, 1.0], [39, 455, 1.0], [39, 456, 1.0], [39, 457, 1.0], [39, 458, 1.0], [39, 459, 1.0], [39, 460, 1.0], [39, 461, 1.0], [39, 462, 1.0], [39, 463, 1.0], [39, 464, 1.0], [39, 465, 1.0], [39, 466, 1.0], [39, 467, 1.0], [40, 455, 1.0], [40, 456, 1.0], [40, 457, 1.0], [40, 458, 1.0], [40, 459, 1.0], [40, 460, 1.0], [40, 461, 1.0], [40, 462, 1.0], [40, 463, 1.0], [40, 464, 1.0], [40, 465, 1.0], [40, 466, 1.0], [41, 455, 1.0], [41, 456, 1.0], [41, 457, 1.0], [41, 458, 1.0], [41, 459, 1.0], [41, 460, 1.0], [41, 461, 1.0], [41, 462, 1.0], [41, 463, 1.0], [41, 464, 1.0], [41, 465, 1.0], [41, 466, 1.0], [42, 455, 1.0], [42, 456, 1.0], [42, 457, 1.0], [42, 458, 1.0], ...] |
| 517473255 | 517473255 | [[41, 493, 1.0], [41, 494, 1.0], [41, 495, 1.0], [41, 496, 1.0], [42, 492, 1.0], [42, 493, 1.0], [42, 494, 1.0], [42, 495, 1.0], [42, 496, 1.0], [42, 497, 1.0], [43, 489, 1.0], [43, 490, 1.0], [43, 491, 1.0], [43, 492, 1.0], [43, 493, 1.0], [43, 494, 1.0], [43, 495, 1.0], [43, 496, 1.0], [43, 497, 1.0], [43, 498, 1.0], [44, 488, 1.0], [44, 489, 1.0], [44, 490, 1.0], [44, 491, 1.0], [44, 492, 1.0], [44, 493, 1.0], [44, 494, 1.0], [44, 495, 1.0], [44, 496, 1.0], [44, 497, 1.0], [44, 498, 1.0], [45, 487, 1.0], [45, 488, 1.0], [45, 489, 1.0], [45, 490, 1.0], [45, 491, 1.0], [45, 492, 1.0], [45, 493, 1.0], [45, 494, 1.0], [45, 495, 1.0], [45, 496, 1.0], [45, 497, 1.0], [45, 498, 1.0], [46, 486, 1.0], [46, 487, 1.0], [46, 488, 1.0], [46, 489, 1.0], [46, 490, 1.0], [46, 491, 1.0], [46, 492, 1.0], [46, 493, 1.0], [46, 494, 1.0], [46, 495, 1.0], [46, 496, 1.0], [46, 497, 1.0], [46, 498, 1.0], [47, 486, 1.0], [47, 487, 1.0], [47, 488, 1.0], [47, 489, 1.0], [47, 490, 1.0], [47, 491, 1.0], [47, 492, 1.0], [47, 493, 1.0], [47, 494, 1.0], [47, 495, 1.0], [47, 496, 1.0], [47, 497, 1.0], [47, 498, 1.0], [47, 499, 1.0], [48, 486, 1.0], [48, 487, 1.0], [48, 488, 1.0], [48, 489, 1.0], [48, 490, 1.0], [48, 491, 1.0], [48, 492, 1.0], [48, 493, 1.0], [48, 494, 1.0], [48, 495, 1.0], [48, 496, 1.0], [48, 497, 1.0], [48, 498, 1.0], [48, 499, 1.0], [49, 486, 1.0], [49, 487, 1.0], [49, 488, 1.0], [49, 489, 1.0], [49, 490, 1.0], [49, 491, 1.0], [49, 492, 1.0], [49, 493, 1.0], [49, 494, 1.0], [49, 495, 1.0], [49, 496, 1.0], [49, 497, 1.0], [49, 498, 1.0], [49, 499, 1.0], [50, 486, 1.0], [50, 487, 1.0], ...] |
... and 170 more rows.
Let's examine the $\Delta F / F$ activity of the first ROI.
df_over_f = nwbfile.processing["ophys"]["DfOverF"]["DfOverF"].data
df_over_f_timestamps = nwbfile.processing["ophys"]["DfOverF"]["DfOverF"].timestamps[...]
df_over_f to the .data, but not slicing it like we did .timestamps[:], we are delaying the downloading of any data until we determine which ROI or frame indices we want to select in the cell below.
roi_index = 0
plot_traces(timestamps=df_over_f_timestamps, traces=df_over_f[:, roi_index], y_axis_label = "$\Delta F \ / \ F$ (a.u.)")
We can examine a narrower time window by slicing the frames (the first axis of the data) - but we have to take care to apply the same frame selection to both the data and the timestamps.
df_over_f_timestamps.shape
(113888,)
frame_slice = slice(10_000, 30_000)
plot_traces(timestamps=df_over_f_timestamps[frame_slice], traces=df_over_f[frame_slice, roi_index], y_axis_label = "$\Delta F \ / \ F$ (a.u.)")
plot_traces(
timestamps=df_over_f_timestamps,
traces=df_over_f[:, 0:50],
figure_shape=(10, 8),
y_axis_label = "$\Delta F \ / \ F$ (a.u.)",
trace_color="gray",
)
It looks like different cells are active at different times. What could that be about?
3.4 Stimulus epochs¶
Several stimuli are shown during each imaging session, interleaved with each other. The stimulus epoch table provides information of these interleaved stimulus epochs.
session_id="501559087" or similar. Most but not all of the sessions in the dataset have an epochs table, so if you have tried to use a different session ID and find it does not have epochs table, please try another session.
epoch_table = nwbfile.epochs
epoch_table
epochs (TimeIntervals)
table
| start_time | stop_time | stimulus_type | |
|---|---|---|---|
| id | |||
| 0 | 51.55381 | 531.93378 | static_gratings |
| 1 | 561.98893 | 1042.44259 | natural_scenes |
| 2 | 1047.42969 | 1342.66703 | spontaneous |
| 3 | 1342.70028 | 1823.72944 | natural_scenes |
... and 4 more rows.
epoch_table.stimulus_type.data[:]
array(['static_gratings', 'natural_scenes', 'spontaneous',
'natural_scenes', 'static_gratings', 'natural_movie_one',
'natural_scenes', 'static_gratings'], dtype=object)
frame = plot_traces(
timestamps=df_over_f_timestamps,
traces=df_over_f[:, 0:50],
figure_shape=(10, 8),
y_axis_label = "$\Delta F \ / \ F$ (a.u.)",
trace_color="gray",
return_frame=True,
)
apply_epoch_shading(frame=frame, epoch_table=epoch_table)
3.5 Running speed¶
This is the running speed of the animal on the rotating disk throughout the entire session.
running_speed = nwbfile.processing["behavior"]["BehavioralTimeSeries"]["running_speed"]
running_speed
running_speed (TimeSeries)
data
timestamps
plot_traces(timestamps=running_speed.timestamps[:], traces=running_speed.data[:], hide_y_scale=False, y_axis_label="Running speed (cm/s)")
plot_traces(
timestamps=df_over_f_timestamps,
traces=df_over_f[:, 0:50],
figure_shape=(14,10),
trace_color="gray",
)
frame = plot_traces(
timestamps=running_speed.timestamps[:],
traces=(0.2 * running_speed.data[:]) - 20, # Apply some custom scaling to get both traces on the same x-axis
reset_figure=False,
return_frame=True,
)
apply_epoch_shading(frame=frame, epoch_table=epoch_table)
Interesting things¶
There are some interesting neurons here!
session_id="501559087" to exactly reproduce the plots as shown on GitHub.
While you should probably do this your first time running the notebook - once you are familiar, you should see if the other 1517 sessions can reproduce the same results!
plot_traces(
timestamps=df_over_f_timestamps,
traces=df_over_f[:, 49],
figure_shape=(14,4),
trace_color="gray",
)
frame = plot_traces(
timestamps=running_speed.timestamps[:],
traces=(0.2 * running_speed.data[:]) - 20, # Apply some custom scaling to get both traces on the same x-axis
reset_figure=False,
return_frame=True,
)
apply_epoch_shading(frame=frame, epoch_table=epoch_table)
plot_traces(
timestamps=df_over_f_timestamps,
traces=df_over_f[:, 4],
figure_shape=(14,4),
trace_color="gray",
)
frame = plot_traces(
timestamps=running_speed.timestamps[:],
traces=(0.2 * running_speed.data[:]) - 20, # Apply some custom scaling to get both traces on the same x-axis
reset_figure=False,
return_frame=True,
)
apply_epoch_shading(frame=frame, epoch_table=epoch_table)
plot_traces(
timestamps=df_over_f_timestamps,
traces=df_over_f[:, 35],
figure_shape=(14,4),
trace_color="gray",
)
frame = plot_traces(
timestamps=running_speed.timestamps[:],
traces=(0.2 * running_speed.data[:]) - 20, # Apply some custom scaling to get both traces on the same x-axis
reset_figure=False,
return_frame=True,
)
apply_epoch_shading(frame=frame, epoch_table=epoch_table)
3.6 Extracted events from $\Delta F / F$¶
Sean Jewell and Daniella Witten developed an L0 method to extract to events from the $\Delta F / F$ traces.
session_id="501559087" or similar. Most but not all of the sessions in the dataset have identified DFOverFEvents events, so if you have tried to use a different session ID and find it does not have this object, please try another session.
df_over_f_events = nwbfile.processing["ophys"]["DfOverF"]["DfOverFEvents"]
df_over_f_events
DfOverFEvents (RoiResponseSeries)
data
timestamps (link to processing/ophys/Fluorescence/Corrected/timestamps)
rois
table
imaging_plane
optical_channel
0
device
grid_spacing
table
| global_roi_id | pixel_mask | |
|---|---|---|
| id | ||
| 517473350 | 517473350 | [[55, 291, 1.0], [55, 292, 1.0], [56, 290, 1.0], [56, 291, 1.0], [56, 292, 1.0], [56, 293, 1.0], [56, 294, 1.0], [56, 295, 1.0], [56, 296, 1.0], [57, 289, 1.0], [57, 290, 1.0], [57, 291, 1.0], [57, 292, 1.0], [57, 293, 1.0], [57, 294, 1.0], [57, 295, 1.0], [57, 296, 1.0], [57, 297, 1.0], [57, 298, 1.0], [58, 288, 1.0], [58, 289, 1.0], [58, 290, 1.0], [58, 291, 1.0], [58, 292, 1.0], [58, 293, 1.0], [58, 294, 1.0], [58, 295, 1.0], [58, 296, 1.0], [58, 297, 1.0], [58, 298, 1.0], [58, 299, 1.0], [59, 288, 1.0], [59, 289, 1.0], [59, 290, 1.0], [59, 291, 1.0], [59, 292, 1.0], [59, 293, 1.0], [59, 294, 1.0], [59, 295, 1.0], [59, 296, 1.0], [59, 297, 1.0], [59, 298, 1.0], [59, 299, 1.0], [60, 288, 1.0], [60, 289, 1.0], [60, 290, 1.0], [60, 291, 1.0], [60, 292, 1.0], [60, 293, 1.0], [60, 294, 1.0], [60, 295, 1.0], [60, 296, 1.0], [60, 297, 1.0], [60, 298, 1.0], [60, 299, 1.0], [61, 287, 1.0], [61, 288, 1.0], [61, 289, 1.0], [61, 290, 1.0], [61, 291, 1.0], [61, 292, 1.0], [61, 293, 1.0], [61, 294, 1.0], [61, 295, 1.0], [61, 296, 1.0], [61, 297, 1.0], [61, 298, 1.0], [61, 299, 1.0], [62, 287, 1.0], [62, 288, 1.0], [62, 289, 1.0], [62, 290, 1.0], [62, 291, 1.0], [62, 292, 1.0], [62, 293, 1.0], [62, 294, 1.0], [62, 295, 1.0], [62, 296, 1.0], [62, 297, 1.0], [62, 298, 1.0], [62, 299, 1.0], [63, 287, 1.0], [63, 288, 1.0], [63, 289, 1.0], [63, 290, 1.0], [63, 291, 1.0], [63, 292, 1.0], [63, 293, 1.0], [63, 294, 1.0], [63, 295, 1.0], [63, 296, 1.0], [63, 297, 1.0], [63, 298, 1.0], [63, 299, 1.0], [64, 287, 1.0], [64, 288, 1.0], [64, 289, 1.0], [64, 290, 1.0], [64, 291, 1.0], [64, 292, 1.0], ...] |
| 517473341 | 517473341 | [[58, 272, 1.0], [58, 273, 1.0], [58, 274, 1.0], [58, 275, 1.0], [58, 276, 1.0], [58, 277, 1.0], [59, 271, 1.0], [59, 272, 1.0], [59, 273, 1.0], [59, 274, 1.0], [59, 275, 1.0], [59, 276, 1.0], [59, 277, 1.0], [59, 278, 1.0], [59, 279, 1.0], [59, 280, 1.0], [59, 281, 1.0], [59, 282, 1.0], [60, 271, 1.0], [60, 272, 1.0], [60, 273, 1.0], [60, 274, 1.0], [60, 275, 1.0], [60, 276, 1.0], [60, 277, 1.0], [60, 278, 1.0], [60, 279, 1.0], [60, 280, 1.0], [60, 281, 1.0], [60, 282, 1.0], [60, 283, 1.0], [61, 270, 1.0], [61, 271, 1.0], [61, 272, 1.0], [61, 273, 1.0], [61, 274, 1.0], [61, 275, 1.0], [61, 276, 1.0], [61, 277, 1.0], [61, 278, 1.0], [61, 279, 1.0], [61, 280, 1.0], [61, 281, 1.0], [61, 282, 1.0], [62, 270, 1.0], [62, 271, 1.0], [62, 272, 1.0], [62, 273, 1.0], [62, 274, 1.0], [62, 275, 1.0], [62, 276, 1.0], [62, 277, 1.0], [62, 278, 1.0], [62, 279, 1.0], [62, 280, 1.0], [62, 281, 1.0], [62, 282, 1.0], [63, 269, 1.0], [63, 270, 1.0], [63, 271, 1.0], [63, 272, 1.0], [63, 273, 1.0], [63, 274, 1.0], [63, 275, 1.0], [63, 276, 1.0], [63, 277, 1.0], [63, 278, 1.0], [63, 279, 1.0], [63, 280, 1.0], [63, 281, 1.0], [63, 282, 1.0], [64, 268, 1.0], [64, 269, 1.0], [64, 270, 1.0], [64, 271, 1.0], [64, 272, 1.0], [64, 273, 1.0], [64, 274, 1.0], [64, 275, 1.0], [64, 276, 1.0], [64, 277, 1.0], [64, 278, 1.0], [64, 279, 1.0], [64, 280, 1.0], [64, 281, 1.0], [64, 282, 1.0], [65, 268, 1.0], [65, 269, 1.0], [65, 270, 1.0], [65, 271, 1.0], [65, 272, 1.0], [65, 273, 1.0], [65, 274, 1.0], [65, 275, 1.0], [65, 276, 1.0], [65, 277, 1.0], [65, 278, 1.0], [65, 279, 1.0], [65, 280, 1.0], [65, 281, 1.0], ...] |
| 517473313 | 517473313 | [[33, 459, 1.0], [33, 460, 1.0], [33, 461, 1.0], [33, 462, 1.0], [33, 463, 1.0], [34, 458, 1.0], [34, 459, 1.0], [34, 460, 1.0], [34, 461, 1.0], [34, 462, 1.0], [34, 463, 1.0], [34, 464, 1.0], [35, 457, 1.0], [35, 458, 1.0], [35, 459, 1.0], [35, 460, 1.0], [35, 461, 1.0], [35, 462, 1.0], [35, 463, 1.0], [35, 464, 1.0], [35, 465, 1.0], [35, 466, 1.0], [36, 456, 1.0], [36, 457, 1.0], [36, 458, 1.0], [36, 459, 1.0], [36, 460, 1.0], [36, 461, 1.0], [36, 462, 1.0], [36, 463, 1.0], [36, 464, 1.0], [36, 465, 1.0], [36, 466, 1.0], [36, 467, 1.0], [37, 456, 1.0], [37, 457, 1.0], [37, 458, 1.0], [37, 459, 1.0], [37, 460, 1.0], [37, 461, 1.0], [37, 462, 1.0], [37, 463, 1.0], [37, 464, 1.0], [37, 465, 1.0], [37, 466, 1.0], [37, 467, 1.0], [38, 455, 1.0], [38, 456, 1.0], [38, 457, 1.0], [38, 458, 1.0], [38, 459, 1.0], [38, 460, 1.0], [38, 461, 1.0], [38, 462, 1.0], [38, 463, 1.0], [38, 464, 1.0], [38, 465, 1.0], [38, 466, 1.0], [38, 467, 1.0], [39, 455, 1.0], [39, 456, 1.0], [39, 457, 1.0], [39, 458, 1.0], [39, 459, 1.0], [39, 460, 1.0], [39, 461, 1.0], [39, 462, 1.0], [39, 463, 1.0], [39, 464, 1.0], [39, 465, 1.0], [39, 466, 1.0], [39, 467, 1.0], [40, 455, 1.0], [40, 456, 1.0], [40, 457, 1.0], [40, 458, 1.0], [40, 459, 1.0], [40, 460, 1.0], [40, 461, 1.0], [40, 462, 1.0], [40, 463, 1.0], [40, 464, 1.0], [40, 465, 1.0], [40, 466, 1.0], [41, 455, 1.0], [41, 456, 1.0], [41, 457, 1.0], [41, 458, 1.0], [41, 459, 1.0], [41, 460, 1.0], [41, 461, 1.0], [41, 462, 1.0], [41, 463, 1.0], [41, 464, 1.0], [41, 465, 1.0], [41, 466, 1.0], [42, 455, 1.0], [42, 456, 1.0], [42, 457, 1.0], [42, 458, 1.0], ...] |
| 517473255 | 517473255 | [[41, 493, 1.0], [41, 494, 1.0], [41, 495, 1.0], [41, 496, 1.0], [42, 492, 1.0], [42, 493, 1.0], [42, 494, 1.0], [42, 495, 1.0], [42, 496, 1.0], [42, 497, 1.0], [43, 489, 1.0], [43, 490, 1.0], [43, 491, 1.0], [43, 492, 1.0], [43, 493, 1.0], [43, 494, 1.0], [43, 495, 1.0], [43, 496, 1.0], [43, 497, 1.0], [43, 498, 1.0], [44, 488, 1.0], [44, 489, 1.0], [44, 490, 1.0], [44, 491, 1.0], [44, 492, 1.0], [44, 493, 1.0], [44, 494, 1.0], [44, 495, 1.0], [44, 496, 1.0], [44, 497, 1.0], [44, 498, 1.0], [45, 487, 1.0], [45, 488, 1.0], [45, 489, 1.0], [45, 490, 1.0], [45, 491, 1.0], [45, 492, 1.0], [45, 493, 1.0], [45, 494, 1.0], [45, 495, 1.0], [45, 496, 1.0], [45, 497, 1.0], [45, 498, 1.0], [46, 486, 1.0], [46, 487, 1.0], [46, 488, 1.0], [46, 489, 1.0], [46, 490, 1.0], [46, 491, 1.0], [46, 492, 1.0], [46, 493, 1.0], [46, 494, 1.0], [46, 495, 1.0], [46, 496, 1.0], [46, 497, 1.0], [46, 498, 1.0], [47, 486, 1.0], [47, 487, 1.0], [47, 488, 1.0], [47, 489, 1.0], [47, 490, 1.0], [47, 491, 1.0], [47, 492, 1.0], [47, 493, 1.0], [47, 494, 1.0], [47, 495, 1.0], [47, 496, 1.0], [47, 497, 1.0], [47, 498, 1.0], [47, 499, 1.0], [48, 486, 1.0], [48, 487, 1.0], [48, 488, 1.0], [48, 489, 1.0], [48, 490, 1.0], [48, 491, 1.0], [48, 492, 1.0], [48, 493, 1.0], [48, 494, 1.0], [48, 495, 1.0], [48, 496, 1.0], [48, 497, 1.0], [48, 498, 1.0], [48, 499, 1.0], [49, 486, 1.0], [49, 487, 1.0], [49, 488, 1.0], [49, 489, 1.0], [49, 490, 1.0], [49, 491, 1.0], [49, 492, 1.0], [49, 493, 1.0], [49, 494, 1.0], [49, 495, 1.0], [49, 496, 1.0], [49, 497, 1.0], [49, 498, 1.0], [49, 499, 1.0], [50, 486, 1.0], [50, 487, 1.0], ...] |
... and 170 more rows.
plot_traces(
timestamps=df_over_f_events.timestamps[:],
traces=2 * df_over_f_events.data[:, 1], # Arbitrarily scale by 2 for better appearance
)
plot_traces(
timestamps=df_over_f_events.timestamps[:],
traces=df_over_f[:, 1] - 5, # Arbitrarily shift by 5 for better appearance
reset_figure=False,
)
plot_traces(
timestamps=df_over_f_events.timestamps[:],
traces=2 * df_over_f_events.data[:, 1], # Arbitrarily scale by 2 for better appearance
)
plot_traces(
timestamps=df_over_f_events.timestamps[:],
traces=df_over_f[:, 1] - 5, # Arbitrarily shift by 5 for better appearance
reset_figure=False,
)
matplotlib_pyplot.xlim(690, 720); # in units seconds
plot_traces(
timestamps=df_over_f_events.timestamps[:],
traces=df_over_f_events.data[:, 0:50],
figure_shape=(14,10),
trace_color="gray",
)
frame = plot_traces(
timestamps=running_speed.timestamps[:],
traces=(0.2 * running_speed.data[:]) - 20, # Apply some custom scaling to get both traces on the same x-axis
reset_figure=False,
return_frame=True,
)
apply_epoch_shading(frame=frame, epoch_table=epoch_table)
3.7 Stimulus Module¶
For each stimulus there is a neurodata object with information about the condition and timing of each trial.
3.7a Natural Scenes¶
For the natural scenes, these are IndexSeries whose data are the indices of the corresponding stimulus template and whose timestamps are the time in seconds when each template was presented.
Some sessions may also have 'locally sparse' noise of varying degrees, which can be accessed in an identical fashion.
natural_scene_presentation = nwbfile.stimulus["natural_scenes_stimulus"]
natural_scene_presentation
natural_scenes_stimulus (IndexSeries)
data
timestamps
natural_scene_presentation = nwbfile.stimulus["natural_scenes_stimulus"]
natural_scene_presentation.data[:]
array([81, 33, 76, ..., 34, 0, 87], dtype=uint32)
natural_scene_presentation.timestamps[:]
array([ 561.98893, 562.25491, 562.48764, ..., 3221.9665 , 3222.23249,
3222.46524])
The actual images and movies presented during the session area also included in the NWB file as the stimulus template.
Stimuli that are generated programmatically (e.g., drifting and static gratings) do not have a stimulus template.
nwbfile.stimulus_template["natural_scenes_template"]
natural_scenes_template (Images)
images
NaturalScene0
NaturalScene1
NaturalScene10
NaturalScene100
NaturalScene101
NaturalScene102
NaturalScene103
NaturalScene104
NaturalScene105
NaturalScene106
NaturalScene107
NaturalScene108
NaturalScene109
NaturalScene11
NaturalScene110
NaturalScene111
NaturalScene112
NaturalScene113
NaturalScene114
NaturalScene115
NaturalScene116
NaturalScene117
NaturalScene12
NaturalScene13
NaturalScene14
NaturalScene15
NaturalScene16
NaturalScene17
NaturalScene18
NaturalScene19
NaturalScene2
NaturalScene20
NaturalScene21
NaturalScene22
NaturalScene23
NaturalScene24
NaturalScene25
NaturalScene26
NaturalScene27
NaturalScene28
NaturalScene29
NaturalScene3
NaturalScene30
NaturalScene31
NaturalScene32
NaturalScene33
NaturalScene34
NaturalScene35
NaturalScene36
NaturalScene37
NaturalScene38
NaturalScene39
NaturalScene4
NaturalScene40
NaturalScene41
NaturalScene42
NaturalScene43
NaturalScene44
NaturalScene45
NaturalScene46
NaturalScene47
NaturalScene48
NaturalScene49
NaturalScene5
NaturalScene50
NaturalScene51
NaturalScene52
NaturalScene53
NaturalScene54
NaturalScene55
NaturalScene56
NaturalScene57
NaturalScene58
NaturalScene59
NaturalScene6
NaturalScene60
NaturalScene61
NaturalScene62
NaturalScene63
NaturalScene64
NaturalScene65
NaturalScene66
NaturalScene67
NaturalScene68
NaturalScene69
NaturalScene7
NaturalScene70
NaturalScene71
NaturalScene72
NaturalScene73
NaturalScene74
NaturalScene75
NaturalScene76
NaturalScene77
NaturalScene78
NaturalScene79
NaturalScene8
NaturalScene80
NaturalScene81
NaturalScene82
NaturalScene83
NaturalScene84
NaturalScene85
NaturalScene86
NaturalScene87
NaturalScene88
NaturalScene89
NaturalScene9
NaturalScene90
NaturalScene91
NaturalScene92
NaturalScene93
NaturalScene94
NaturalScene95
NaturalScene96
NaturalScene97
NaturalScene98
NaturalScene99
natural_scene_template = nwbfile.stimulus_template["natural_scenes_template"]
natural_scene_template
natural_scenes_template (Images)
images
NaturalScene0
NaturalScene1
NaturalScene10
NaturalScene100
NaturalScene101
NaturalScene102
NaturalScene103
NaturalScene104
NaturalScene105
NaturalScene106
NaturalScene107
NaturalScene108
NaturalScene109
NaturalScene11
NaturalScene110
NaturalScene111
NaturalScene112
NaturalScene113
NaturalScene114
NaturalScene115
NaturalScene116
NaturalScene117
NaturalScene12
NaturalScene13
NaturalScene14
NaturalScene15
NaturalScene16
NaturalScene17
NaturalScene18
NaturalScene19
NaturalScene2
NaturalScene20
NaturalScene21
NaturalScene22
NaturalScene23
NaturalScene24
NaturalScene25
NaturalScene26
NaturalScene27
NaturalScene28
NaturalScene29
NaturalScene3
NaturalScene30
NaturalScene31
NaturalScene32
NaturalScene33
NaturalScene34
NaturalScene35
NaturalScene36
NaturalScene37
NaturalScene38
NaturalScene39
NaturalScene4
NaturalScene40
NaturalScene41
NaturalScene42
NaturalScene43
NaturalScene44
NaturalScene45
NaturalScene46
NaturalScene47
NaturalScene48
NaturalScene49
NaturalScene5
NaturalScene50
NaturalScene51
NaturalScene52
NaturalScene53
NaturalScene54
NaturalScene55
NaturalScene56
NaturalScene57
NaturalScene58
NaturalScene59
NaturalScene6
NaturalScene60
NaturalScene61
NaturalScene62
NaturalScene63
NaturalScene64
NaturalScene65
NaturalScene66
NaturalScene67
NaturalScene68
NaturalScene69
NaturalScene7
NaturalScene70
NaturalScene71
NaturalScene72
NaturalScene73
NaturalScene74
NaturalScene75
NaturalScene76
NaturalScene77
NaturalScene78
NaturalScene79
NaturalScene8
NaturalScene80
NaturalScene81
NaturalScene82
NaturalScene83
NaturalScene84
NaturalScene85
NaturalScene86
NaturalScene87
NaturalScene88
NaturalScene89
NaturalScene9
NaturalScene90
NaturalScene91
NaturalScene92
NaturalScene93
NaturalScene94
NaturalScene95
NaturalScene96
NaturalScene97
NaturalScene98
NaturalScene99
len(natural_scene_template.images)
118
scene_number = 101
plot_image(image=natural_scene_template[f"NaturalScene{scene_number}"], color_map="gray")
Use the stimulus table to plot the responses of cell index 1 to image 101
3.7b Natural Movies¶
The natural movies are very similar to the natural scenes, except that their data are the indices to a single ImageSeries (instead of multiple Image objects stored in an Image container) found in the templates.
natural_movie_one_presentation = nwbfile.stimulus["natural_movie_one_stimulus"]
natural_movie_one_presentation
natural_movie_one_stimulus (IndexSeries)
data
timestamps
indexed_timeseries
data
device
natural_movie_one_template = nwbfile.stimulus_template["natural_movie_one"]
natural_movie_one_template
natural_movie_one (ImageSeries)
data
device
natural_movie_one_template.data.shape
(900, 304, 608)
frame_index = 101
plot_image(image=natural_movie_one_template.data[frame_index, ...], color_map="gray")
3.7c Static Gratings¶
For the static gratings, these are simple a table with the corresponding parameters that were varied on each presentation.
static_gratings_table = nwbfile.stimulus["static_gratings"]
static_gratings_table.to_dataframe()
| start_time | stop_time | orientation_in_degrees | spatial_frequency_in_cycles_per_degree | phase | is_blank_sweep | |
|---|---|---|---|---|---|---|
| id | ||||||
| 0 | 51.55381 | 51.80381 | 90.0 | 0.04 | 0.5 | False |
| 1 | 51.78653 | 52.03653 | 150.0 | 0.04 | 0.5 | False |
| 2 | 52.05250 | 52.30250 | 30.0 | 0.02 | 0.0 | False |
You can always access descriptions of such columns if you're wondering about what each one means in the context of the experiment.
static_gratings_column_descriptions = {column.name: column.description for column in static_gratings_table.columns}
static_gratings_column_descriptions
{'start_time': 'Start time of epoch, in seconds',
'stop_time': 'Stop time of epoch, in seconds',
'orientation_in_degrees': 'Angle of the grating in degrees. NaN values correspond to a blank sweep.',
'spatial_frequency_in_cycles_per_degree': 'Period of the grating in cycles/degree. NaN values correspond to a blank sweep.',
'phase': 'Relative position of the grating. Phase 0 and Phase 0.5 are 180° apart so that the peak of the grating of phase 0 lines up with the trough of phase 0.5. NaN values correspond to a blank sweep.',
'is_blank_sweep': 'Mean luminance gray image.'}
3.7 Drifting Gratings¶
To examine the drifting gratings, we will have to open a stream to a separate session file.
The drifting gratings are also a table with the corresponding parameters that were varied on each presentation.
session_id_with_drifting_gratings = "501940850"
nwbfile_with_drifting_gratings = read_visual_coding_ophys_file(dandiset=dandiset, session_id=session_id_with_drifting_gratings)
drifting_gratings = nwbfile_with_drifting_gratings.stimulus["drifting_gratings"]
drifting_gratings
drifting_gratings (TimeIntervals)
table
| start_time | stop_time | orientation_in_degrees | spatial_frequency_in_cycles_per_degree | temporal_frequency_in_hz | is_blank_sweep | |
|---|---|---|---|---|---|---|
| id | ||||||
| 0 | 34.35150 | 36.35150 | 135.0 | 0.04 | 1.0 | False |
| 1 | 37.33713 | 39.33713 | 0.0 | 0.04 | 2.0 | False |
| 2 | 40.35595 | 42.35595 | 0.0 | 0.04 | 8.0 | False |
| 3 | 43.34159 | 45.34159 | 225.0 | 0.04 | 8.0 | False |
... and 624 more rows.
drifting_gratings_column_descriptions = {column.name: column.description for column in drifting_gratings.columns}
drifting_gratings_column_descriptions
{'start_time': 'Start time of epoch, in seconds',
'stop_time': 'Stop time of epoch, in seconds',
'orientation_in_degrees': 'Angle of the grating in degrees. NaN values correspond to a blank sweep.',
'spatial_frequency_in_cycles_per_degree': 'Period of the grating in cycles/degree. NaN values correspond to a blank sweep.',
'temporal_frequency_in_hz': 'The speed at which the grating moves in Hz. NaN values correspond to a blank sweep.',
'is_blank_sweep': 'Mean luminance gray image.'}
Part 4: Compute activity aligned to stimulus presentations
But it can also decrease the speed of repeated operations - so we'll go ahead and load all the data using
[...] for these next steps!
df_over_f = df_over_f[...]
scene_number = 101
cell_index = 1
times_when_scene_was_presented = natural_scene_presentation.timestamps[natural_scene_presentation.data[:] == scene_number]
# Applying 30 frames of buffer room before and 50 after
df_over_f_times_to_frames = numpy.searchsorted(df_over_f_timestamps, times_when_scene_was_presented)
for df_over_f_time_to_frame in df_over_f_times_to_frames[:]:
subset_df_over_f_frames = slice(df_over_f_time_to_frame - 30, df_over_f_time_to_frame + 50)
timestamps = df_over_f_timestamps[subset_df_over_f_frames]
aligned_timestamps = timestamps - timestamps[30]
frame = plot_traces(
timestamps=aligned_timestamps,
traces=df_over_f[subset_df_over_f_frames, cell_index],
x_axis_label="Time aligned to stimulus onset (s)",
y_axis_label="$\Delta F \ / \ F$ (a.u.)",
reset_figure=False,
return_frame=True,
)
# Here, we use the fact about duration of the stimulusu learned from the description of the NaturalScene objects
frame.axvspan(aligned_timestamps[30], aligned_timestamps[37], color="gray", alpha=0.3);
scene_number = 101
cell_index = 1
times_when_scene_was_presented = natural_scene_presentation.timestamps[natural_scene_presentation.data[:] == scene_number]
# Applying 30 frames of buffer room before and 50 after
df_over_f_times_to_frames = numpy.searchsorted(df_over_f_timestamps, times_when_scene_was_presented)
timestamps = df_over_f_timestamps[subset_df_over_f_frames]
aligned_timestamps = timestamps - timestamps[30]
for presentation_index, df_over_f_time_to_frame in enumerate(df_over_f_times_to_frames[:], start=1):
subset_df_over_f_frames = slice(df_over_f_time_to_frame - 30, df_over_f_time_to_frame + 50)
frame = plot_traces(
timestamps=aligned_timestamps,
traces=2 * df_over_f_events.data[subset_df_over_f_frames, cell_index] + presentation_index,
reset_figure=False,
x_axis_label="Time aligned to stimulus onset (s)",
y_axis_label="$\Delta F \ / \ F$ (a.u.)",
return_frame=True,
)
# Here, we use the fact about duration of the stimulusu learned from the description of the NaturalScene objects
frame.axvspan(aligned_timestamps[30], aligned_timestamps[37], color="gray", alpha=0.3);
Part 5: Compute the signal correlations as a function of distance between neurons
number_of_rois = df_over_f.shape[1]
number_of_natural_scenes = len(natural_scene_template.images)
response = numpy.empty(shape=(number_of_rois, number_of_natural_scenes))
for roi_index in range(number_of_rois):
for scene_number in range(number_of_natural_scenes):
times_when_scene_was_presented = natural_scene_presentation.timestamps[natural_scene_presentation.data[:] == scene_number]
df_over_f_times_to_frames = numpy.searchsorted(df_over_f_timestamps, times_when_scene_was_presented)
# Take the mean over a very short period just after stimulus onset (15 frames)
frames_before_onset = 0
frames_after_onset = 15
temp = numpy.empty(shape=(frames_after_onset + frames_before_onset, len(times_when_scene_was_presented)))
for temp_index, df_over_f_time_to_frame in enumerate(df_over_f_times_to_frames):
subset_df_over_f_frames = slice(df_over_f_time_to_frame - frames_before_onset, df_over_f_time_to_frame + frames_after_onset)
temp[:, temp_index] = df_over_f[subset_df_over_f_frames, roi_index]
response[roi_index, scene_number] = temp.mean()
matplotlib_pyplot.plot(response[0,:], 'o-');
This is the correlation of the mean response to all images.
signal_correlations = numpy.empty(shape=(number_of_rois, number_of_rois))
for roi_index_1 in range(number_of_rois):
for roi_index_2 in range(number_of_rois):
r, p = scipy_stats.pearsonr(response[roi_index_1, :], response[roi_index_2, :])
signal_correlations[roi_index_1, roi_index_2] = r
matplotlib_pyplot.imshow(signal_correlations);
pixel_masks = plane_segmentation["pixel_mask"][:]
centroid_x = numpy.zeros(shape=number_of_rois)
centroid_y = numpy.zeros(shape=number_of_rois)
for roi_index, pixel_mask in enumerate(pixel_masks):
centroid_x[roi_index] = numpy.mean([pixel[0] for pixel in pixel_mask])
centroid_y[roi_index] = numpy.mean([pixel[1] for pixel in pixel_mask])
distance = numpy.zeros(shape=(number_of_rois, number_of_rois))
for roi_index_1 in range(number_of_rois):
for roi_index_2 in range(number_of_rois):
distance[roi_index_1, roi_index_2] = numpy.sqrt(
(centroid_x[roi_index_1] - centroid_x[roi_index_2])**2 + (centroid_y[roi_index_1] - centroid_y[roi_index_2])**2
)
matplotlib_pyplot.imshow(distance);
# Only use the upper half of the matrix due to symmetry of correlation
indices = numpy.triu_indices(number_of_rois, k=1)
matplotlib_pyplot.figure(figsize=(8,6))
matplotlib_pyplot.plot(distance[indices[0], indices[1]], signal_correlations[indices[0], indices[1]], ".", markersize=3.0)
matplotlib_pyplot.xlabel("Distance (pixels)", fontsize=16)
matplotlib_pyplot.ylabel("Signal correlation", fontsize=16);
Conclusion
The Allen Brain Observatory - Visual Coding (Ophys) is a rich dataset that allows you to explore single cell and population responses across 6 cortical areas, 4 cortical layers, 14 transgenic Cre lines to a range of different visual stimuli.
This tutorial should enable you to get started in using these data including finding experiments and accessing the neural and behavioral data, along with stimulus information.